File size: 54,882 Bytes
8537242
 
9b7c23b
 
 
 
8537242
9b7c23b
 
 
 
7b55466
9b7c23b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7b55466
 
8537242
9b7c23b
 
8537242
9b7c23b
 
8537242
9b7c23b
 
 
 
8537242
9b7c23b
 
 
 
 
8537242
9b7c23b
 
 
 
 
 
 
 
e18a102
b577163
5ea5863
 
 
3c89eff
 
 
 
 
 
 
 
 
b577163
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
b577163
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
3cc829a
 
 
 
 
 
 
 
 
3c89eff
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
3cc829a
 
3c89eff
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
b577163
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
b577163
 
 
3cc829a
3c89eff
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
b577163
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
b577163
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
3cc829a
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
b577163
 
 
 
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
b577163
 
3c89eff
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
3cc829a
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3cc829a
 
 
3c89eff
 
 
 
 
 
 
 
 
3cc829a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c89eff
 
2d9fb04
 
 
 
 
 
 
 
 
 
 
 
8537242
 
 
 
 
0d03fdb
8537242
 
9843332
 
8537242
 
9843332
 
8537242
 
 
 
 
 
 
 
 
 
 
 
 
0db3f6c
8537242
 
 
 
a63c355
1192627
8537242
 
 
1192627
 
 
 
 
 
 
 
 
 
 
 
8537242
 
 
 
 
 
 
 
0d03fdb
8537242
 
1192627
8537242
0d03fdb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8537242
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
1192627
8537242
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1192627
 
 
 
 
 
 
 
 
8537242
 
 
1192627
 
 
 
 
 
 
 
 
 
 
 
8537242
 
 
 
 
 
 
 
1192627
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8537242
0db3f6c
 
 
5ea5863
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
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
---
annotations_creators:
- crowdsourced
- expert-generated
- found
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- found
- machine-generated
language:
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- it
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
license:
- other
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- extended|conll2003
- extended|squad
- extended|xnli
- original
task_categories:
- question-answering
- summarization
- text-classification
- text2text-generation
- token-classification
task_ids:
- acceptability-classification
- extractive-qa
- named-entity-recognition
- natural-language-inference
- news-articles-headline-generation
- open-domain-qa
- parsing
- topic-classification
pretty_name: XGLUE
license_details: Licence Universal Dependencies v2.5
tags:
- paraphrase-identification
- question-answering
dataset_info:
- config_name: ner
  features:
  - name: words
    sequence: string
  - name: ner
    sequence:
      class_label:
        names:
          '0': O
          '1': B-PER
          '2': I-PER
          '3': B-ORG
          '4': I-ORG
          '5': B-LOC
          '6': I-LOC
          '7': B-MISC
          '8': I-MISC
  splits:
  - name: train
    num_bytes: 3445854
    num_examples: 14042
  - name: validation.en
    num_bytes: 866569
    num_examples: 3252
  - name: validation.de
    num_bytes: 917967
    num_examples: 2874
  - name: validation.es
    num_bytes: 888551
    num_examples: 1923
  - name: validation.nl
    num_bytes: 659144
    num_examples: 2895
  - name: test.en
    num_bytes: 784976
    num_examples: 3454
  - name: test.de
    num_bytes: 922741
    num_examples: 3007
  - name: test.es
    num_bytes: 864804
    num_examples: 1523
  - name: test.nl
    num_bytes: 1196660
    num_examples: 5202
  download_size: 875905871
  dataset_size: 10547266
- config_name: pos
  features:
  - name: words
    sequence: string
  - name: pos
    sequence:
      class_label:
        names:
          '0': ADJ
          '1': ADP
          '2': ADV
          '3': AUX
          '4': CCONJ
          '5': DET
          '6': INTJ
          '7': NOUN
          '8': NUM
          '9': PART
          '10': PRON
          '11': PROPN
          '12': PUNCT
          '13': SCONJ
          '14': SYM
          '15': VERB
          '16': X
  splits:
  - name: train
    num_bytes: 7279459
    num_examples: 25376
  - name: validation.en
    num_bytes: 421410
    num_examples: 2001
  - name: validation.de
    num_bytes: 219328
    num_examples: 798
  - name: validation.es
    num_bytes: 620491
    num_examples: 1399
  - name: validation.nl
    num_bytes: 198003
    num_examples: 717
  - name: validation.bg
    num_bytes: 346802
    num_examples: 1114
  - name: validation.el
    num_bytes: 229447
    num_examples: 402
  - name: validation.fr
    num_bytes: 600964
    num_examples: 1475
  - name: validation.pl
    num_bytes: 620694
    num_examples: 2214
  - name: validation.tr
    num_bytes: 186196
    num_examples: 987
  - name: validation.vi
    num_bytes: 203669
    num_examples: 799
  - name: validation.zh
    num_bytes: 212579
    num_examples: 499
  - name: validation.ur
    num_bytes: 284016
    num_examples: 551
  - name: validation.hi
    num_bytes: 838700
    num_examples: 1658
  - name: validation.it
    num_bytes: 198608
    num_examples: 563
  - name: validation.ar
    num_bytes: 592943
    num_examples: 908
  - name: validation.ru
    num_bytes: 261563
    num_examples: 578
  - name: validation.th
    num_bytes: 272834
    num_examples: 497
  - name: test.en
    num_bytes: 420613
    num_examples: 2076
  - name: test.de
    num_bytes: 291759
    num_examples: 976
  - name: test.es
    num_bytes: 200003
    num_examples: 425
  - name: test.nl
    num_bytes: 193337
    num_examples: 595
  - name: test.bg
    num_bytes: 339460
    num_examples: 1115
  - name: test.el
    num_bytes: 235137
    num_examples: 455
  - name: test.fr
    num_bytes: 166865
    num_examples: 415
  - name: test.pl
    num_bytes: 600534
    num_examples: 2214
  - name: test.tr
    num_bytes: 186519
    num_examples: 982
  - name: test.vi
    num_bytes: 211408
    num_examples: 799
  - name: test.zh
    num_bytes: 202055
    num_examples: 499
  - name: test.ur
    num_bytes: 288189
    num_examples: 534
  - name: test.hi
    num_bytes: 839659
    num_examples: 1683
  - name: test.it
    num_bytes: 173861
    num_examples: 481
  - name: test.ar
    num_bytes: 561709
    num_examples: 679
  - name: test.ru
    num_bytes: 255393
    num_examples: 600
  - name: test.th
    num_bytes: 272834
    num_examples: 497
  download_size: 875905871
  dataset_size: 19027041
- config_name: mlqa
  features:
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: answers
    sequence:
    - name: answer_start
      dtype: int32
    - name: text
      dtype: string
  splits:
  - name: train
    num_bytes: 75307933
    num_examples: 87599
  - name: validation.en
    num_bytes: 1255587
    num_examples: 1148
  - name: validation.de
    num_bytes: 454258
    num_examples: 512
  - name: validation.ar
    num_bytes: 785493
    num_examples: 517
  - name: validation.es
    num_bytes: 388625
    num_examples: 500
  - name: validation.hi
    num_bytes: 1092167
    num_examples: 507
  - name: validation.vi
    num_bytes: 692227
    num_examples: 511
  - name: validation.zh
    num_bytes: 411213
    num_examples: 504
  - name: test.en
    num_bytes: 13264513
    num_examples: 11590
  - name: test.de
    num_bytes: 4070659
    num_examples: 4517
  - name: test.ar
    num_bytes: 7976090
    num_examples: 5335
  - name: test.es
    num_bytes: 4044224
    num_examples: 5253
  - name: test.hi
    num_bytes: 11385051
    num_examples: 4918
  - name: test.vi
    num_bytes: 7559078
    num_examples: 5495
  - name: test.zh
    num_bytes: 4092921
    num_examples: 5137
  download_size: 875905871
  dataset_size: 132780039
- config_name: nc
  features:
  - name: news_title
    dtype: string
  - name: news_body
    dtype: string
  - name: news_category
    dtype:
      class_label:
        names:
          '0': foodanddrink
          '1': sports
          '2': travel
          '3': finance
          '4': lifestyle
          '5': news
          '6': entertainment
          '7': health
          '8': video
          '9': autos
  splits:
  - name: train
    num_bytes: 280615806
    num_examples: 100000
  - name: validation.en
    num_bytes: 33389140
    num_examples: 10000
  - name: validation.de
    num_bytes: 26757254
    num_examples: 10000
  - name: validation.es
    num_bytes: 31781308
    num_examples: 10000
  - name: validation.fr
    num_bytes: 27154099
    num_examples: 10000
  - name: validation.ru
    num_bytes: 46053007
    num_examples: 10000
  - name: test.en
    num_bytes: 34437987
    num_examples: 10000
  - name: test.de
    num_bytes: 26632007
    num_examples: 10000
  - name: test.es
    num_bytes: 31350078
    num_examples: 10000
  - name: test.fr
    num_bytes: 27589545
    num_examples: 10000
  - name: test.ru
    num_bytes: 46183830
    num_examples: 10000
  download_size: 875905871
  dataset_size: 611944061
- config_name: xnli
  features:
  - name: premise
    dtype: string
  - name: hypothesis
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': entailment
          '1': neutral
          '2': contradiction
  splits:
  - name: train
    num_bytes: 74444346
    num_examples: 392702
  - name: validation.en
    num_bytes: 433471
    num_examples: 2490
  - name: validation.ar
    num_bytes: 633009
    num_examples: 2490
  - name: validation.bg
    num_bytes: 774069
    num_examples: 2490
  - name: validation.de
    num_bytes: 494612
    num_examples: 2490
  - name: validation.el
    num_bytes: 841234
    num_examples: 2490
  - name: validation.es
    num_bytes: 478430
    num_examples: 2490
  - name: validation.fr
    num_bytes: 510112
    num_examples: 2490
  - name: validation.hi
    num_bytes: 1023923
    num_examples: 2490
  - name: validation.ru
    num_bytes: 786450
    num_examples: 2490
  - name: validation.sw
    num_bytes: 429858
    num_examples: 2490
  - name: validation.th
    num_bytes: 1061168
    num_examples: 2490
  - name: validation.tr
    num_bytes: 459316
    num_examples: 2490
  - name: validation.ur
    num_bytes: 699960
    num_examples: 2490
  - name: validation.vi
    num_bytes: 590688
    num_examples: 2490
  - name: validation.zh
    num_bytes: 384859
    num_examples: 2490
  - name: test.en
    num_bytes: 875142
    num_examples: 5010
  - name: test.ar
    num_bytes: 1294561
    num_examples: 5010
  - name: test.bg
    num_bytes: 1573042
    num_examples: 5010
  - name: test.de
    num_bytes: 996487
    num_examples: 5010
  - name: test.el
    num_bytes: 1704793
    num_examples: 5010
  - name: test.es
    num_bytes: 969821
    num_examples: 5010
  - name: test.fr
    num_bytes: 1029247
    num_examples: 5010
  - name: test.hi
    num_bytes: 2073081
    num_examples: 5010
  - name: test.ru
    num_bytes: 1603474
    num_examples: 5010
  - name: test.sw
    num_bytes: 871659
    num_examples: 5010
  - name: test.th
    num_bytes: 2147023
    num_examples: 5010
  - name: test.tr
    num_bytes: 934942
    num_examples: 5010
  - name: test.ur
    num_bytes: 1416246
    num_examples: 5010
  - name: test.vi
    num_bytes: 1190225
    num_examples: 5010
  - name: test.zh
    num_bytes: 777937
    num_examples: 5010
  download_size: 875905871
  dataset_size: 103503185
- config_name: paws-x
  features:
  - name: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': different
          '1': same
  splits:
  - name: train
    num_bytes: 12018349
    num_examples: 49401
  - name: validation.en
    num_bytes: 484287
    num_examples: 2000
  - name: validation.de
    num_bytes: 506009
    num_examples: 2000
  - name: validation.es
    num_bytes: 505888
    num_examples: 2000
  - name: validation.fr
    num_bytes: 525031
    num_examples: 2000
  - name: test.en
    num_bytes: 486734
    num_examples: 2000
  - name: test.de
    num_bytes: 516214
    num_examples: 2000
  - name: test.es
    num_bytes: 511111
    num_examples: 2000
  - name: test.fr
    num_bytes: 527101
    num_examples: 2000
  download_size: 875905871
  dataset_size: 16080724
- config_name: qadsm
  features:
  - name: query
    dtype: string
  - name: ad_title
    dtype: string
  - name: ad_description
    dtype: string
  - name: relevance_label
    dtype:
      class_label:
        names:
          '0': Bad
          '1': Good
  splits:
  - name: train
    num_bytes: 12528141
    num_examples: 100000
  - name: validation.en
    num_bytes: 1248839
    num_examples: 10000
  - name: validation.de
    num_bytes: 1566011
    num_examples: 10000
  - name: validation.fr
    num_bytes: 1651804
    num_examples: 10000
  - name: test.en
    num_bytes: 1236997
    num_examples: 10000
  - name: test.de
    num_bytes: 1563985
    num_examples: 10000
  - name: test.fr
    num_bytes: 1594118
    num_examples: 10000
  download_size: 875905871
  dataset_size: 21389895
- config_name: wpr
  features:
  - name: query
    dtype: string
  - name: web_page_title
    dtype: string
  - name: web_page_snippet
    dtype: string
  - name: relavance_label
    dtype:
      class_label:
        names:
          '0': Bad
          '1': Fair
          '2': Good
          '3': Excellent
          '4': Perfect
  splits:
  - name: train
    num_bytes: 33885931
    num_examples: 99997
  - name: validation.en
    num_bytes: 3417760
    num_examples: 10008
  - name: validation.de
    num_bytes: 2929029
    num_examples: 10004
  - name: validation.es
    num_bytes: 2451026
    num_examples: 10004
  - name: validation.fr
    num_bytes: 3055899
    num_examples: 10005
  - name: validation.it
    num_bytes: 2416388
    num_examples: 10003
  - name: validation.pt
    num_bytes: 2449797
    num_examples: 10001
  - name: validation.zh
    num_bytes: 3118577
    num_examples: 10002
  - name: test.en
    num_bytes: 3402487
    num_examples: 10004
  - name: test.de
    num_bytes: 2923577
    num_examples: 9997
  - name: test.es
    num_bytes: 2422895
    num_examples: 10006
  - name: test.fr
    num_bytes: 3059392
    num_examples: 10020
  - name: test.it
    num_bytes: 2403736
    num_examples: 10001
  - name: test.pt
    num_bytes: 2462350
    num_examples: 10015
  - name: test.zh
    num_bytes: 3141598
    num_examples: 9999
  download_size: 875905871
  dataset_size: 73540442
- config_name: qam
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': 'False'
          '1': 'True'
  splits:
  - name: train
    num_bytes: 28357964
    num_examples: 100000
  - name: validation.en
    num_bytes: 3085501
    num_examples: 10000
  - name: validation.de
    num_bytes: 3304031
    num_examples: 10000
  - name: validation.fr
    num_bytes: 3142833
    num_examples: 10000
  - name: test.en
    num_bytes: 3082297
    num_examples: 10000
  - name: test.de
    num_bytes: 3309496
    num_examples: 10000
  - name: test.fr
    num_bytes: 3140213
    num_examples: 10000
  download_size: 875905871
  dataset_size: 47422335
- config_name: qg
  features:
  - name: answer_passage
    dtype: string
  - name: question
    dtype: string
  splits:
  - name: train
    num_bytes: 27464034
    num_examples: 100000
  - name: validation.en
    num_bytes: 3047040
    num_examples: 10000
  - name: validation.de
    num_bytes: 3270877
    num_examples: 10000
  - name: validation.es
    num_bytes: 3341775
    num_examples: 10000
  - name: validation.fr
    num_bytes: 3175615
    num_examples: 10000
  - name: validation.it
    num_bytes: 3191193
    num_examples: 10000
  - name: validation.pt
    num_bytes: 3328434
    num_examples: 10000
  - name: test.en
    num_bytes: 3043813
    num_examples: 10000
  - name: test.de
    num_bytes: 3270190
    num_examples: 10000
  - name: test.es
    num_bytes: 3353522
    num_examples: 10000
  - name: test.fr
    num_bytes: 3178352
    num_examples: 10000
  - name: test.it
    num_bytes: 3195684
    num_examples: 10000
  - name: test.pt
    num_bytes: 3340296
    num_examples: 10000
  download_size: 875905871
  dataset_size: 66200825
- config_name: ntg
  features:
  - name: news_body
    dtype: string
  - name: news_title
    dtype: string
  splits:
  - name: train
    num_bytes: 890709581
    num_examples: 300000
  - name: validation.en
    num_bytes: 34317076
    num_examples: 10000
  - name: validation.de
    num_bytes: 27404379
    num_examples: 10000
  - name: validation.es
    num_bytes: 30896109
    num_examples: 10000
  - name: validation.fr
    num_bytes: 27261523
    num_examples: 10000
  - name: validation.ru
    num_bytes: 43247386
    num_examples: 10000
  - name: test.en
    num_bytes: 33697284
    num_examples: 10000
  - name: test.de
    num_bytes: 26738202
    num_examples: 10000
  - name: test.es
    num_bytes: 31111489
    num_examples: 10000
  - name: test.fr
    num_bytes: 26997447
    num_examples: 10000
  - name: test.ru
    num_bytes: 44050350
    num_examples: 10000
  download_size: 875905871
  dataset_size: 1216430826
config_names:
- mlqa
- nc
- ner
- ntg
- paws-x
- pos
- qadsm
- qam
- qg
- wpr
- xnli
---

# Dataset Card for XGLUE

## Table of Contents
- [Table of Contents](#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

- **Homepage:** [XGLUE homepage](https://microsoft.github.io/XGLUE/)
- **Paper:** [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://arxiv.org/abs/2004.01401)
- **Point of Contact:** [[email protected]](mailto:[email protected]?subject=XGLUE Feedback)

### Dataset Summary

XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained models with respect to
cross-lingual natural language understanding and generation.

XGLUE is composed of 11 tasks spans 19 languages. For each task, the training data is only available in English.
This means that to succeed at XGLUE, a model must have a strong zero-shot cross-lingual transfer capability to learn
from the English data of a specific task and transfer what it learned to other languages. Comparing to its concurrent
work XTREME, XGLUE has two characteristics: First, it includes cross-lingual NLU and cross-lingual NLG tasks at the
same time; Second, besides including 5 existing cross-lingual tasks (i.e. NER, POS, MLQA, PAWS-X and XNLI), XGLUE
selects 6 new tasks from Bing scenarios as well, including News Classification (NC), Query-Ad Matching (QADSM),
Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). Such diversities
of languages, tasks and task origin provide a comprehensive benchmark for quantifying the quality of a pre-trained
model on cross-lingual natural language understanding and generation.

The training data of each task is in English while the validation and test data is present in multiple different languages.
The following table shows which languages are present as validation and test data for each config.

![Available Languages for Test and Validation Data](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xglue_langs.png)

Therefore, for each config, a cross-lingual pre-trained model should be fine-tuned on the English training data, and evaluated on for all languages.

### Supported Tasks and Leaderboards

The XGLUE leaderboard can be found on the [homepage](https://microsoft.github.io/XGLUE/) and 
consists of a XGLUE-Understanding Score (the average of the tasks `ner`, `pos`, `mlqa`, `nc`, `xnli`, `paws-x`, `qadsm`, `wpr`, `qam`) and a XGLUE-Generation Score (the average of the tasks `qg`, `ntg`).

### Languages

For all tasks (configurations), the "train" split is in English (`en`).

For each task, the "validation" and "test" splits are present in these languages:
- ner: `en`, `de`, `es`, `nl`
- pos: `en`, `de`, `es`, `nl`, `bg`, `el`, `fr`, `pl`, `tr`, `vi`, `zh`, `ur`, `hi`, `it`, `ar`, `ru`, `th`
- mlqa: `en`, `de`, `ar`, `es`, `hi`, `vi`, `zh`
- nc: `en`, `de`, `es`, `fr`, `ru`
- xnli: `en`, `ar`, `bg`, `de`, `el`, `es`, `fr`, `hi`, `ru`, `sw`, `th`, `tr`, `ur`, `vi`, `zh`
- paws-x: `en`, `de`, `es`, `fr`
- qadsm: `en`, `de`, `fr`
- wpr: `en`, `de`, `es`, `fr`, `it`, `pt`, `zh`
- qam: `en`, `de`, `fr`
- qg: `en`, `de`, `es`, `fr`, `it`, `pt`
- ntg: `en`, `de`, `es`, `fr`, `ru`

## Dataset Structure

### Data Instances

#### ner

An example of 'test.nl' looks as follows.

```json
{
  "ner": [
    "O",
    "O",
    "O",
    "B-LOC",
    "O",
    "B-LOC",
    "O",
    "B-LOC",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "O",
    "B-PER",
    "I-PER",
    "O",
    "O",
    "B-LOC",
    "O",
    "O"
  ],
  "words": [
    "Dat",
    "is",
    "in",
    "Itali\u00eb",
    ",",
    "Spanje",
    "of",
    "Engeland",
    "misschien",
    "geen",
    "probleem",
    ",",
    "maar",
    "volgens",
    "'",
    "Der",
    "Kaiser",
    "'",
    "in",
    "Duitsland",
    "wel",
    "."
  ]
}
```

#### pos

An example of 'test.fr' looks as follows.

```json
{
  "pos": [
    "PRON",
    "VERB",
    "SCONJ",
    "ADP",
    "PRON",
    "CCONJ",
    "DET",
    "NOUN",
    "ADP",
    "NOUN",
    "CCONJ",
    "NOUN",
    "ADJ",
    "PRON",
    "PRON",
    "AUX",
    "ADV",
    "VERB",
    "PUNCT",
    "PRON",
    "VERB",
    "VERB",
    "DET",
    "ADJ",
    "NOUN",
    "ADP",
    "DET",
    "NOUN",
    "PUNCT"
  ],
  "words": [
    "Je",
    "sens",
    "qu'",
    "entre",
    "\u00e7a",
    "et",
    "les",
    "films",
    "de",
    "m\u00e9decins",
    "et",
    "scientifiques",
    "fous",
    "que",
    "nous",
    "avons",
    "d\u00e9j\u00e0",
    "vus",
    ",",
    "nous",
    "pourrions",
    "emprunter",
    "un",
    "autre",
    "chemin",
    "pour",
    "l'",
    "origine",
    "."
  ]
}
```

#### mlqa

An example of 'test.hi' looks as follows.

```json
{
  "answers": {
    "answer_start": [
      378
    ],
    "text": [
      "\u0909\u0924\u094d\u0924\u0930 \u092a\u0942\u0930\u094d\u0935"
    ]
  },
  "context": "\u0909\u0938\u0940 \"\u090f\u0930\u093f\u092f\u093e XX \" \u0928\u093e\u092e\u0915\u0930\u0923 \u092a\u094d\u0930\u0923\u093e\u0932\u0940 \u0915\u093e \u092a\u094d\u0930\u092f\u094b\u0917 \u0928\u0947\u0935\u093e\u0926\u093e \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u094d\u0925\u0932 \u0915\u0947 \u0905\u0928\u094d\u092f \u092d\u093e\u0917\u094b\u0902 \u0915\u0947 \u0932\u093f\u090f \u0915\u093f\u092f\u093e \u0917\u092f\u093e \u0939\u0948\u0964\u092e\u0942\u0932 \u0930\u0942\u092a \u092e\u0947\u0902 6 \u092c\u091f\u0947 10 \u092e\u0940\u0932 \u0915\u093e \u092f\u0939 \u0906\u092f\u0924\u093e\u0915\u093e\u0930 \u0905\u0921\u094d\u0921\u093e \u0905\u092c \u0924\u0925\u093e\u0915\u0925\u093f\u0924 '\u0917\u094d\u0930\u0942\u092e \u092c\u0949\u0915\u094d\u0938 \" \u0915\u093e \u090f\u0915 \u092d\u093e\u0917 \u0939\u0948, \u091c\u094b \u0915\u093f 23 \u092c\u091f\u0947 25.3 \u092e\u0940\u0932 \u0915\u093e \u090f\u0915 \u092a\u094d\u0930\u0924\u093f\u092c\u0902\u0927\u093f\u0924 \u0939\u0935\u093e\u0908 \u0915\u094d\u0937\u0947\u0924\u094d\u0930 \u0939\u0948\u0964 \u092f\u0939 \u0915\u094d\u0937\u0947\u0924\u094d\u0930 NTS \u0915\u0947 \u0906\u0902\u0924\u0930\u093f\u0915 \u0938\u0921\u093c\u0915 \u092a\u094d\u0930\u092c\u0902\u0927\u0928 \u0938\u0947 \u091c\u0941\u0921\u093c\u093e \u0939\u0948, \u091c\u093f\u0938\u0915\u0940 \u092a\u0915\u094d\u0915\u0940 \u0938\u0921\u093c\u0915\u0947\u0902 \u0926\u0915\u094d\u0937\u093f\u0923 \u092e\u0947\u0902 \u092e\u0930\u0915\u0930\u0940 \u0915\u0940 \u0913\u0930 \u0914\u0930 \u092a\u0936\u094d\u091a\u093f\u092e \u092e\u0947\u0902 \u092f\u0941\u0915\u094d\u0915\u093e \u092b\u094d\u0932\u0948\u091f \u0915\u0940 \u0913\u0930 \u091c\u093e\u0924\u0940 \u0939\u0948\u0902\u0964 \u091d\u0940\u0932 \u0938\u0947 \u0909\u0924\u094d\u0924\u0930 \u092a\u0942\u0930\u094d\u0935 \u0915\u0940 \u0913\u0930 \u092c\u0922\u093c\u0924\u0947 \u0939\u0941\u090f \u0935\u094d\u092f\u093e\u092a\u0915 \u0914\u0930 \u0914\u0930 \u0938\u0941\u0935\u094d\u092f\u0935\u0938\u094d\u0925\u093f\u0924 \u0917\u094d\u0930\u0942\u092e \u091d\u0940\u0932 \u0915\u0940 \u0938\u0921\u093c\u0915\u0947\u0902 \u090f\u0915 \u0926\u0930\u094d\u0930\u0947 \u0915\u0947 \u091c\u0930\u093f\u092f\u0947 \u092a\u0947\u091a\u0940\u0926\u093e \u092a\u0939\u093e\u0921\u093c\u093f\u092f\u094b\u0902 \u0938\u0947 \u0939\u094b\u0915\u0930 \u0917\u0941\u091c\u0930\u0924\u0940 \u0939\u0948\u0902\u0964 \u092a\u0939\u0932\u0947 \u0938\u0921\u093c\u0915\u0947\u0902 \u0917\u094d\u0930\u0942\u092e \u0918\u093e\u091f\u0940",
  "question": "\u091d\u0940\u0932 \u0915\u0947 \u0938\u093e\u092a\u0947\u0915\u094d\u0937 \u0917\u094d\u0930\u0942\u092e \u0932\u0947\u0915 \u0930\u094b\u0921 \u0915\u0939\u093e\u0901 \u091c\u093e\u0924\u0940 \u0925\u0940?"
}
```

#### nc

An example of 'test.es' looks as follows.

```json
{
  "news_body": "El bizcocho es seguramente el producto m\u00e1s b\u00e1sico y sencillo de toda la reposter\u00eda : consiste en poco m\u00e1s que mezclar unos cuantos ingredientes, meterlos al horno y esperar a que se hagan. Por obra y gracia del impulsor qu\u00edmico, tambi\u00e9n conocido como \"levadura de tipo Royal\", despu\u00e9s de un rato de calorcito esta combinaci\u00f3n de harina, az\u00facar, huevo, grasa -aceite o mantequilla- y l\u00e1cteo se transforma en uno de los productos m\u00e1s deliciosos que existen para desayunar o merendar . Por muy manazas que seas, es m\u00e1s que probable que tu bizcocho casero supere en calidad a cualquier infamia industrial envasada. Para lograr un bizcocho digno de admiraci\u00f3n s\u00f3lo tienes que respetar unas pocas normas que afectan a los ingredientes, proporciones, mezclado, horneado y desmoldado. Todas las tienes resumidas en unos dos minutos el v\u00eddeo de arriba, en el que adem \u00e1s aprender\u00e1s alg\u00fan truquillo para que tu bizcochaco quede m\u00e1s fino, jugoso, esponjoso y amoroso. M\u00e1s en MSN:",
  "news_category": "foodanddrink",
  "news_title": "Cocina para lerdos: las leyes del bizcocho"
}
```

#### xnli

An example of 'validation.th' looks as follows.

```json
{
  "hypothesis": "\u0e40\u0e02\u0e32\u0e42\u0e17\u0e23\u0e2b\u0e32\u0e40\u0e40\u0e21\u0e48\u0e02\u0e2d\u0e07\u0e40\u0e02\u0e32\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e23\u0e27\u0e14\u0e40\u0e23\u0e47\u0e27\u0e2b\u0e25\u0e31\u0e07\u0e08\u0e32\u0e01\u0e17\u0e35\u0e48\u0e23\u0e16\u0e42\u0e23\u0e07\u0e40\u0e23\u0e35\u0e22\u0e19\u0e2a\u0e48\u0e07\u0e40\u0e02\u0e32\u0e40\u0e40\u0e25\u0e49\u0e27",
  "label": 1,
  "premise": "\u0e41\u0e25\u0e30\u0e40\u0e02\u0e32\u0e1e\u0e39\u0e14\u0e27\u0e48\u0e32, \u0e21\u0e48\u0e32\u0e21\u0e4a\u0e32 \u0e1c\u0e21\u0e2d\u0e22\u0e39\u0e48\u0e1a\u0e49\u0e32\u0e19"
}
```

#### paws-x

An example of 'test.es' looks as follows.

```json
{
  "label": 1,
  "sentence1": "La excepci\u00f3n fue entre fines de 2005 y 2009 cuando jug\u00f3 en Suecia con Carlstad United BK, Serbia con FK Borac \u010ca\u010dak y el FC Terek Grozny de Rusia.",
  "sentence2": "La excepci\u00f3n se dio entre fines del 2005 y 2009, cuando jug\u00f3 con Suecia en el Carlstad United BK, Serbia con el FK Borac \u010ca\u010dak y el FC Terek Grozny de Rusia."
}
```

#### qadsm

An example of 'train' looks as follows.

```json
{
  "ad_description": "Your New England Cruise Awaits! Holland America Line Official Site.",
  "ad_title": "New England Cruises",
  "query": "cruise portland maine",
  "relevance_label": 1
}
```

#### wpr

An example of 'test.zh' looks as follows.

```json
{
  "query": "maxpro\u5b98\u7f51",
  "relavance_label": 0,
  "web_page_snippet": "\u5728\u7ebf\u8d2d\u4e70\uff0c\u552e\u540e\u670d\u52a1\u3002vivo\u667a\u80fd\u624b\u673a\u5f53\u5b63\u660e\u661f\u673a\u578b\u6709NEX\uff0cvivo X21\uff0cvivo X20\uff0c\uff0cvivo X23\u7b49\uff0c\u5728vivo\u5b98\u7f51\u8d2d\u4e70\u624b\u673a\u53ef\u4ee5\u4eab\u53d712 \u671f\u514d\u606f\u4ed8\u6b3e\u3002 \u54c1\u724c Funtouch OS \u4f53\u9a8c\u5e97 | ...",
  "wed_page_title": "vivo\u667a\u80fd\u624b\u673a\u5b98\u65b9\u7f51\u7ad9-AI\u975e\u51e1\u6444\u5f71X23"
}
```

#### qam

An example of 'validation.en' looks as follows.

```json
{
  "annswer": "Erikson has stated that after the last novel of the Malazan Book of the Fallen was finished, he and Esslemont would write a comprehensive guide tentatively named The Encyclopaedia Malazica.",
  "label": 0,
  "question": "main character of malazan book of the fallen"
}
```

#### qg

An example of 'test.de' looks as follows.

```json
{
  "answer_passage": "Medien bei WhatsApp automatisch speichern. Tippen Sie oben rechts unter WhatsApp auf die drei Punkte oder auf die Men\u00fc-Taste Ihres Smartphones. Dort wechseln Sie in die \"Einstellungen\" und von hier aus weiter zu den \"Chat-Einstellungen\". Unter dem Punkt \"Medien Auto-Download\" k\u00f6nnen Sie festlegen, wann die WhatsApp-Bilder heruntergeladen werden sollen.",
  "question": "speichenn von whats app bilder unterbinden"
}
```

#### ntg

An example of 'test.en' looks as follows.

```json
{
  "news_body": "Check out this vintage Willys Pickup! As they say, the devil is in the details, and it's not every day you see such attention paid to every last area of a restoration like with this 1961 Willys Pickup . Already the Pickup has a unique look that shares some styling with the Jeep, plus some original touches you don't get anywhere else. It's a classy way to show up to any event, all thanks to Hollywood Motors . A burgundy paint job contrasts with white lower panels and the roof. Plenty of tasteful chrome details grace the exterior, including the bumpers, headlight bezels, crossmembers on the grille, hood latches, taillight bezels, exhaust finisher, tailgate hinges, etc. Steel wheels painted white and chrome hubs are a tasteful addition. Beautiful oak side steps and bed strips add a touch of craftsmanship to this ride. This truck is of real showroom quality, thanks to the astoundingly detailed restoration work performed on it, making this Willys Pickup a fierce contender for best of show. Under that beautiful hood is a 225 Buick V6 engine mated to a three-speed manual transmission, so you enjoy an ideal level of control. Four wheel drive is functional, making it that much more utilitarian and downright cool. The tires are new, so you can enjoy a lot of life out of them, while the wheels and hubs are in great condition. Just in case, a fifth wheel with a tire and a side mount are included. Just as important, this Pickup runs smoothly, so you can go cruising or even hit the open road if you're interested in participating in some classic rallies. You might associate Willys with the famous Jeep CJ, but the automaker did produce a fair amount of trucks. The Pickup is quite the unique example, thanks to distinct styling that really turns heads, making it a favorite at quite a few shows. Source: Hollywood Motors Check These Rides Out Too: Fear No Trails With These Off-Roaders 1965 Pontiac GTO: American Icon For Sale In Canada Low-Mileage 1955 Chevy 3100 Represents Turn In Pickup Market",
  "news_title": "This 1961 Willys Pickup Will Let You Cruise In Style"
}
```

### Data Fields

#### ner

In the following each data field in ner is explained. The data fields are the same among all splits.

- `words`: a list of words composing the sentence.
- `ner`: a list of entitity classes corresponding to each word respectively.


#### pos

In the following each data field in pos is explained. The data fields are the same among all splits.

- `words`: a list of words composing the sentence.
- `pos`: a list of "part-of-speech" classes corresponding to each word respectively.


#### mlqa

In the following each data field in mlqa is explained. The data fields are the same among all splits.

- `context`: a string, the context containing the answer.
- `question`: a string, the question to be answered.
- `answers`: a string, the answer to `question`.


#### nc

In the following each data field in nc is explained. The data fields are the same among all splits.

- `news_title`: a string, to the title of the news report.
- `news_body`: a string, to the actual news report.
- `news_category`: a string, the category of the news report, *e.g.* `foodanddrink`


#### xnli

In the following each data field in xnli is explained. The data fields are the same among all splits.

- `premise`: a string, the context/premise, *i.e.* the first sentence for natural language inference.
- `hypothesis`: a string, a sentence whereas its relation to `premise` is to be classified, *i.e.* the second sentence for natural language inference.
- `label`: a class catory (int), natural language inference relation class between `hypothesis` and `premise`. One of 0: entailment, 1: contradiction, 2: neutral.


#### paws-x

In the following each data field in paws-x is explained. The data fields are the same among all splits.

- `sentence1`: a string, a sentence.
- `sentence2`: a string, a sentence whereas the sentence is either a paraphrase of `sentence1` or not.
- `label`: a class label (int), whether `sentence2` is a paraphrase of `sentence1` One of 0: different, 1: same.


#### qadsm

In the following each data field in qadsm is explained. The data fields are the same among all splits.

- `query`: a string, the search query one would insert into a search engine.
- `ad_title`: a string, the title of the advertisement.
- `ad_description`: a string, the content of the advertisement, *i.e.* the main body.
- `relevance_label`: a class label (int), how relevant the advertisement `ad_title` + `ad_description` is to the search query `query`. One of 0: Bad, 1: Good.


#### wpr

In the following each data field in wpr is explained. The data fields are the same among all splits.

- `query`: a string, the search query one would insert into a search engine.
- `web_page_title`: a string, the title of a web page.
- `web_page_snippet`: a string, the content of a web page, *i.e.* the main body.
- `relavance_label`: a class label (int), how relevant the web page `web_page_snippet` + `web_page_snippet` is to the search query `query`. One of 0: Bad, 1: Fair, 2: Good, 3: Excellent, 4: Perfect.


#### qam

In the following each data field in qam is explained. The data fields are the same among all splits.

- `question`: a string, a question.
- `answer`: a string, a possible answer to `question`.
- `label`: a class label (int), whether the `answer` is relevant to the `question`. One of 0: False, 1: True.


#### qg

In the following each data field in qg is explained. The data fields are the same among all splits.

- `answer_passage`: a string, a detailed answer to the `question`.
- `question`: a string, a question.


#### ntg

In the following each data field in ntg is explained. The data fields are the same among all splits.

- `news_body`: a string, the content of a news article.
- `news_title`: a string, the title corresponding to the news article `news_body`.


### Data Splits

#### ner

The following table shows the number of data samples/number of rows for each split in ner.

|   |train|validation.en|validation.de|validation.es|validation.nl|test.en|test.de|test.es|test.nl|
|---|----:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|
|ner|14042|         3252|         2874|         1923|         2895|   3454|   3007|   1523|   5202|


#### pos

The following table shows the number of data samples/number of rows for each split in pos.

|   |train|validation.en|validation.de|validation.es|validation.nl|validation.bg|validation.el|validation.fr|validation.pl|validation.tr|validation.vi|validation.zh|validation.ur|validation.hi|validation.it|validation.ar|validation.ru|validation.th|test.en|test.de|test.es|test.nl|test.bg|test.el|test.fr|test.pl|test.tr|test.vi|test.zh|test.ur|test.hi|test.it|test.ar|test.ru|test.th|
|---|----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|
|pos|25376|         2001|          798|         1399|          717|         1114|          402|         1475|         2214|          987|          799|          499|          551|         1658|          563|          908|          578|          497|   2076|    976|    425|    595|   1115|    455|    415|   2214|    982|    799|    499|    534|   1683|    481|    679|    600|    497|


#### mlqa

The following table shows the number of data samples/number of rows for each split in mlqa.

|    |train|validation.en|validation.de|validation.ar|validation.es|validation.hi|validation.vi|validation.zh|test.en|test.de|test.ar|test.es|test.hi|test.vi|test.zh|
|----|----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|
|mlqa|87599|         1148|          512|          517|          500|          507|          511|          504|  11590|   4517|   5335|   5253|   4918|   5495|   5137|


#### nc

The following table shows the number of data samples/number of rows for each split in nc.

|   |train |validation.en|validation.de|validation.es|validation.fr|validation.ru|test.en|test.de|test.es|test.fr|test.ru|
|---|-----:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|
|nc |100000|        10000|        10000|        10000|        10000|        10000|  10000|  10000|  10000|  10000|  10000|


#### xnli

The following table shows the number of data samples/number of rows for each split in xnli.

|    |train |validation.en|validation.ar|validation.bg|validation.de|validation.el|validation.es|validation.fr|validation.hi|validation.ru|validation.sw|validation.th|validation.tr|validation.ur|validation.vi|validation.zh|test.en|test.ar|test.bg|test.de|test.el|test.es|test.fr|test.hi|test.ru|test.sw|test.th|test.tr|test.ur|test.vi|test.zh|
|----|-----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|
|xnli|392702|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|


#### nc

The following table shows the number of data samples/number of rows for each split in nc.

|   |train |validation.en|validation.de|validation.es|validation.fr|validation.ru|test.en|test.de|test.es|test.fr|test.ru|
|---|-----:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|
|nc |100000|        10000|        10000|        10000|        10000|        10000|  10000|  10000|  10000|  10000|  10000|


#### xnli

The following table shows the number of data samples/number of rows for each split in xnli.

|    |train |validation.en|validation.ar|validation.bg|validation.de|validation.el|validation.es|validation.fr|validation.hi|validation.ru|validation.sw|validation.th|validation.tr|validation.ur|validation.vi|validation.zh|test.en|test.ar|test.bg|test.de|test.el|test.es|test.fr|test.hi|test.ru|test.sw|test.th|test.tr|test.ur|test.vi|test.zh|
|----|-----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|
|xnli|392702|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|         2490|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|   5010|


#### paws-x

The following table shows the number of data samples/number of rows for each split in paws-x.

|      |train|validation.en|validation.de|validation.es|validation.fr|test.en|test.de|test.es|test.fr|
|------|----:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|
|paws-x|49401|         2000|         2000|         2000|         2000|   2000|   2000|   2000|   2000|


#### qadsm

The following table shows the number of data samples/number of rows for each split in qadsm.

|     |train |validation.en|validation.de|validation.fr|test.en|test.de|test.fr|
|-----|-----:|------------:|------------:|------------:|------:|------:|------:|
|qadsm|100000|        10000|        10000|        10000|  10000|  10000|  10000|


#### wpr

The following table shows the number of data samples/number of rows for each split in wpr.

|   |train|validation.en|validation.de|validation.es|validation.fr|validation.it|validation.pt|validation.zh|test.en|test.de|test.es|test.fr|test.it|test.pt|test.zh|
|---|----:|------------:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|------:|
|wpr|99997|        10008|        10004|        10004|        10005|        10003|        10001|        10002|  10004|   9997|  10006|  10020|  10001|  10015|   9999|


#### qam

The following table shows the number of data samples/number of rows for each split in qam.

|   |train |validation.en|validation.de|validation.fr|test.en|test.de|test.fr|
|---|-----:|------------:|------------:|------------:|------:|------:|------:|
|qam|100000|        10000|        10000|        10000|  10000|  10000|  10000|


#### qg

The following table shows the number of data samples/number of rows for each split in qg.

|   |train |validation.en|validation.de|validation.es|validation.fr|validation.it|validation.pt|test.en|test.de|test.es|test.fr|test.it|test.pt|
|---|-----:|------------:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|------:|
|qg |100000|        10000|        10000|        10000|        10000|        10000|        10000|  10000|  10000|  10000|  10000|  10000|  10000|


#### ntg

The following table shows the number of data samples/number of rows for each split in ntg.

|   |train |validation.en|validation.de|validation.es|validation.fr|validation.ru|test.en|test.de|test.es|test.fr|test.ru|
|---|-----:|------------:|------------:|------------:|------------:|------------:|------:|------:|------:|------:|------:|
|ntg|300000|        10000|        10000|        10000|        10000|        10000|  10000|  10000|  10000|  10000|  10000|

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

[More Information Needed]

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

The dataset is maintained mainly by Yaobo Liang, Yeyun Gong, Nan Duan, Ming Gong, Linjun Shou, and Daniel Campos from Microsoft Research.

### Licensing Information

The XGLUE datasets are intended for non-commercial research purposes only to promote advancement in the field of
artificial intelligence and related areas, and is made available free of charge without extending any license or other
intellectual property rights. The dataset is provided “as is” without warranty and usage of the data has risks since we
may not own the underlying rights in the documents. We are not be liable for any damages related to use of the dataset.
Feedback is voluntarily given and can be used as we see fit. Upon violation of any of these terms, your rights to use
the dataset will end automatically.

If you have questions about use of the dataset or any research outputs in your products or services, we encourage you
to undertake your own independent legal review. For other questions, please feel free to contact us.

### Citation Information

If you use this dataset, please cite it. Additionally, since XGLUE is also built out of exiting 5 datasets, please
ensure you cite all of them.

An example:
```
We evaluate our model using the XGLUE benchmark \cite{Liang2020XGLUEAN}, a cross-lingual evaluation benchmark
consiting of Named Entity Resolution (NER) \cite{Sang2002IntroductionTT} \cite{Sang2003IntroductionTT},
Part of Speech Tagging (POS) \cite{11234/1-3105}, News Classification (NC), MLQA \cite{Lewis2019MLQAEC},
XNLI \cite{Conneau2018XNLIEC}, PAWS-X \cite{Yang2019PAWSXAC}, Query-Ad Matching (QADSM), Web Page Ranking (WPR),
QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG).
```

```
@article{Liang2020XGLUEAN,
  title={XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation},
  author={Yaobo Liang and Nan Duan and Yeyun Gong and Ning Wu and Fenfei Guo and Weizhen Qi and Ming Gong and Linjun Shou and Daxin Jiang and Guihong Cao and Xiaodong Fan and Ruofei Zhang and Rahul Agrawal and Edward Cui and Sining Wei and Taroon Bharti and Ying Qiao and Jiun-Hung Chen and Winnie Wu and Shuguang Liu and Fan Yang and Daniel Campos and Rangan Majumder and Ming Zhou},
  journal={arXiv},
  year={2020},
  volume={abs/2004.01401}
}

@misc{11234/1-3105,
  title={Universal Dependencies 2.5},
  author={Zeman, Daniel and Nivre, Joakim and Abrams, Mitchell and Aepli, No{\"e}mi and Agi{\'c}, {\v Z}eljko and Ahrenberg, Lars and Aleksandravi{\v c}i{\=u}t{\.e}, Gabriel{\.e} and Antonsen, Lene and Aplonova, Katya and Aranzabe, Maria Jesus and Arutie, Gashaw and Asahara, Masayuki and Ateyah, Luma and Attia, Mohammed and Atutxa, Aitziber and Augustinus, Liesbeth and Badmaeva, Elena and Ballesteros, Miguel and Banerjee, Esha and Bank, Sebastian and Barbu Mititelu, Verginica and Basmov, Victoria and Batchelor, Colin and Bauer, John and Bellato, Sandra and Bengoetxea, Kepa and Berzak, Yevgeni and Bhat, Irshad Ahmad and Bhat, Riyaz Ahmad and Biagetti, Erica and Bick, Eckhard and Bielinskien{\.e}, Agn{\.e} and Blokland, Rogier and Bobicev, Victoria and Boizou, Lo{\"{\i}}c and Borges V{\"o}lker, Emanuel and B{\"o}rstell, Carl and Bosco, Cristina and Bouma, Gosse and Bowman, Sam and Boyd, Adriane and Brokait{\.e}, Kristina and Burchardt, Aljoscha and Candito, Marie and Caron, Bernard and Caron, Gauthier and Cavalcanti, Tatiana and Cebiro{\u g}lu Eryi{\u g}it, G{\"u}l{\c s}en and Cecchini, Flavio Massimiliano and Celano, Giuseppe G. A. and {\v C}{\'e}pl{\"o}, Slavom{\'{\i}}r and Cetin, Savas and Chalub, Fabricio and Choi, Jinho and Cho, Yongseok and Chun, Jayeol and Cignarella, Alessandra T. and Cinkov{\'a}, Silvie and Collomb, Aur{\'e}lie and {\c C}{\"o}ltekin, {\c C}a{\u g}r{\i} and Connor, Miriam and Courtin, Marine and Davidson, Elizabeth and de Marneffe, Marie-Catherine and de Paiva, Valeria and de Souza, Elvis and Diaz de Ilarraza, Arantza and Dickerson, Carly and Dione, Bamba and Dirix, Peter and Dobrovoljc, Kaja and Dozat, Timothy and Droganova, Kira and Dwivedi, Puneet and Eckhoff, Hanne and Eli, Marhaba and Elkahky, Ali and Ephrem, Binyam and Erina, Olga and Erjavec, Toma{\v z} and Etienne, Aline and Evelyn, Wograine and Farkas, Rich{\'a}rd and Fernandez Alcalde, Hector and Foster, Jennifer and Freitas, Cl{\'a}udia and Fujita, Kazunori and Gajdo{\v s}ov{\'a}, Katar{\'{\i}}na and Galbraith, Daniel and Garcia, Marcos and G{\"a}rdenfors, Moa and Garza, Sebastian and Gerdes, Kim and Ginter, Filip and Goenaga, Iakes and Gojenola, Koldo and G{\"o}k{\i}rmak, Memduh and Goldberg, Yoav and G{\'o}mez Guinovart, Xavier and Gonz{\'a}lez Saavedra, Berta and Grici{\=u}t{\.e}, Bernadeta and Grioni, Matias and Gr{\=u}z{\={\i}}tis, Normunds and Guillaume, Bruno and Guillot-Barbance, C{\'e}line and Habash, Nizar and Haji{\v c}, Jan and Haji{\v c} jr., Jan and H{\"a}m{\"a}l{\"a}inen, Mika and H{\`a} M{\~y}, Linh and Han, Na-Rae and Harris, Kim and Haug, Dag and Heinecke, Johannes and Hennig, Felix and Hladk{\'a}, Barbora and Hlav{\'a}{\v c}ov{\'a}, Jaroslava and Hociung, Florinel and Hohle, Petter and Hwang, Jena and Ikeda, Takumi and Ion, Radu and Irimia, Elena and Ishola, {\d O}l{\'a}j{\'{\i}}d{\'e} and Jel{\'{\i}}nek, Tom{\'a}{\v s} and Johannsen, Anders and J{\o}rgensen, Fredrik and Juutinen, Markus and Ka{\c s}{\i}kara, H{\"u}ner and Kaasen, Andre and Kabaeva, Nadezhda and Kahane, Sylvain and Kanayama, Hiroshi and Kanerva, Jenna and Katz, Boris and Kayadelen, Tolga and Kenney, Jessica and Kettnerov{\'a}, V{\'a}clava and Kirchner, Jesse and Klementieva, Elena and K{\"o}hn, Arne and Kopacewicz, Kamil and Kotsyba, Natalia and Kovalevskait{\.e}, Jolanta and Krek, Simon and Kwak, Sookyoung and Laippala, Veronika and Lambertino, Lorenzo and Lam, Lucia and Lando, Tatiana and Larasati, Septina Dian and Lavrentiev, Alexei and Lee, John and L{\^e} H{\`{\^o}}ng, Phương and Lenci, Alessandro and Lertpradit, Saran and Leung, Herman and Li, Cheuk Ying and Li, Josie and Li, Keying and Lim, {KyungTae} and Liovina, Maria and Li, Yuan and Ljube{\v s}i{\'c}, Nikola and Loginova, Olga and Lyashevskaya, Olga and Lynn, Teresa and Macketanz, Vivien and Makazhanov, Aibek and Mandl, Michael and Manning, Christopher and Manurung, Ruli and M{\u a}r{\u a}nduc, C{\u a}t{\u a}lina and Mare{\v c}ek, David and Marheinecke, Katrin and Mart{\'{\i}}nez Alonso, H{\'e}ctor and Martins, Andr{\'e} and Ma{\v s}ek, Jan and Matsumoto, Yuji and {McDonald}, Ryan and {McGuinness}, Sarah and Mendon{\c c}a, Gustavo and Miekka, Niko and Misirpashayeva, Margarita and Missil{\"a}, Anna and Mititelu, C{\u a}t{\u a}lin and Mitrofan, Maria and Miyao, Yusuke and Montemagni, Simonetta and More, Amir and Moreno Romero, Laura and Mori, Keiko Sophie and Morioka, Tomohiko and Mori, Shinsuke and Moro, Shigeki and Mortensen, Bjartur and Moskalevskyi, Bohdan and Muischnek, Kadri and Munro, Robert and Murawaki, Yugo and M{\"u}{\"u}risep, Kaili and Nainwani, Pinkey and Navarro Hor{\~n}iacek, Juan Ignacio and Nedoluzhko, Anna and Ne{\v s}pore-B{\=e}rzkalne, Gunta and Nguy{\~{\^e}}n Th{\d i}, Lương and Nguy{\~{\^e}}n Th{\d i} Minh, Huy{\`{\^e}}n and Nikaido, Yoshihiro and Nikolaev, Vitaly and Nitisaroj, Rattima and Nurmi, Hanna and Ojala, Stina and Ojha, Atul Kr. and Ol{\'u}{\`o}kun, Ad{\'e}day{\d o}̀ and Omura, Mai and Osenova, Petya and {\"O}stling, Robert and {\O}vrelid, Lilja and Partanen, Niko and Pascual, Elena and Passarotti, Marco and Patejuk, Agnieszka and Paulino-Passos, Guilherme and Peljak-{\L}api{\'n}ska, Angelika and Peng, Siyao and Perez, Cenel-Augusto and Perrier, Guy and Petrova, Daria and Petrov, Slav and Phelan, Jason and Piitulainen, Jussi and Pirinen, Tommi A and Pitler, Emily and Plank, Barbara and Poibeau, Thierry and Ponomareva, Larisa and Popel, Martin and Pretkalni{\c n}a, Lauma and Pr{\'e}vost, Sophie and Prokopidis, Prokopis and Przepi{\'o}rkowski, Adam and Puolakainen, Tiina and Pyysalo, Sampo and Qi, Peng and R{\"a}{\"a}bis, Andriela and Rademaker, Alexandre and Ramasamy, Loganathan and Rama, Taraka and Ramisch, Carlos and Ravishankar, Vinit and Real, Livy and Reddy, Siva and Rehm, Georg and Riabov, Ivan and Rie{\ss}ler, Michael and Rimkut{\.e}, Erika and Rinaldi, Larissa and Rituma, Laura and Rocha, Luisa and Romanenko, Mykhailo and Rosa, Rudolf and Rovati, Davide and Roșca, Valentin and Rudina, Olga and Rueter, Jack and Sadde, Shoval and Sagot, Beno{\^{\i}}t and Saleh, Shadi and Salomoni, Alessio and Samard{\v z}i{\'c}, Tanja and Samson, Stephanie and Sanguinetti, Manuela and S{\"a}rg, Dage and Saul{\={\i}}te, Baiba and Sawanakunanon, Yanin and Schneider, Nathan and Schuster, Sebastian and Seddah, Djam{\'e} and Seeker, Wolfgang and Seraji, Mojgan and Shen, Mo and Shimada, Atsuko and Shirasu, Hiroyuki and Shohibussirri, Muh and Sichinava, Dmitry and Silveira, Aline and Silveira, Natalia and Simi, Maria and Simionescu, Radu and Simk{\'o}, Katalin and {\v S}imkov{\'a}, M{\'a}ria and Simov, Kiril and Smith, Aaron and Soares-Bastos, Isabela and Spadine, Carolyn and Stella, Antonio and Straka, Milan and Strnadov{\'a}, Jana and Suhr, Alane and Sulubacak, Umut and Suzuki, Shingo and Sz{\'a}nt{\'o}, Zsolt and Taji, Dima and Takahashi, Yuta and Tamburini, Fabio and Tanaka, Takaaki and Tellier, Isabelle and Thomas, Guillaume and Torga, Liisi and Trosterud, Trond and Trukhina, Anna and Tsarfaty, Reut and Tyers, Francis and Uematsu, Sumire and Ure{\v s}ov{\'a}, Zde{\v n}ka and Uria, Larraitz and Uszkoreit, Hans and Utka, Andrius and Vajjala, Sowmya and van Niekerk, Daniel and van Noord, Gertjan and Varga, Viktor and Villemonte de la Clergerie, Eric and Vincze, Veronika and Wallin, Lars and Walsh, Abigail and Wang, Jing Xian and Washington, Jonathan North and Wendt, Maximilan and Williams, Seyi and Wir{\'e}n, Mats and Wittern, Christian and Woldemariam, Tsegay and Wong, Tak-sum and Wr{\'o}blewska, Alina and Yako, Mary and Yamazaki, Naoki and Yan, Chunxiao and Yasuoka, Koichi and Yavrumyan, Marat M. and Yu, Zhuoran and {\v Z}abokrtsk{\'y}, Zden{\v e}k and Zeldes, Amir and Zhang, Manying and Zhu, Hanzhi},
  url={http://hdl.handle.net/11234/1-3105},
  note={{LINDAT}/{CLARIAH}-{CZ} digital library at the Institute of Formal and Applied Linguistics ({{\'U}FAL}), Faculty of Mathematics and Physics, Charles University},
  copyright={Licence Universal Dependencies v2.5},
  year={2019}
}

@article{Sang2003IntroductionTT,
  title={Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition},
  author={Erik F. Tjong Kim Sang and Fien De Meulder},
  journal={ArXiv},
  year={2003},
  volume={cs.CL/0306050}
}

@article{Sang2002IntroductionTT,
  title={Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition},
  author={Erik F. Tjong Kim Sang},
  journal={ArXiv},
  year={2002},
  volume={cs.CL/0209010}
}

@inproceedings{Conneau2018XNLIEC,
  title={XNLI: Evaluating Cross-lingual Sentence Representations},
  author={Alexis Conneau and Guillaume Lample and Ruty Rinott and Adina Williams and Samuel R. Bowman and Holger Schwenk and Veselin Stoyanov},
  booktitle={EMNLP},
  year={2018}
}

@article{Lewis2019MLQAEC,
  title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
  author={Patrick Lewis and Barlas Oguz and Ruty Rinott and Sebastian Riedel and Holger Schwenk},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.07475}
}

@article{Yang2019PAWSXAC,
  title={PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification},
  author={Yinfei Yang and Yuan Zhang and Chris Tar and Jason Baldridge},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.11828}
}
```

### Contributions

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