File size: 50,265 Bytes
3c0fefa
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
{
    "boolq": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nBoolQ (Boolean Questions, Clark et al., 2019a) is a QA task where each example consists of a short\npassage and a yes/no question about the passage. The questions are provided anonymously and\nunsolicited by users of the Google search engine, and afterwards paired with a paragraph from a\nWikipedia article containing the answer. Following the original work, we evaluate with accuracy.",
        "citation": "@inproceedings{clark2019boolq,\n  title={BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions},\n  author={Clark, Christopher and Lee, Kenton and Chang, Ming-Wei, and Kwiatkowski, Tom and Collins, Michael, and Toutanova, Kristina},\n  booktitle={NAACL},\n  year={2019}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://github.com/google-research-datasets/boolean-questions",
        "license": "",
        "features": {
            "question": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "passage": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "False",
                    "True"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "boolq",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 2107997,
                "num_examples": 3245,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 6179206,
                "num_examples": 9427,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 2118505,
                "num_examples": 3270,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/BoolQ.zip": {
                "num_bytes": 4118001,
                "checksum": "853fbe7922f70c59629f06a39e8d9ca440c3d740e760fd3b87a5ddf3dcba2436"
            }
        },
        "download_size": 4118001,
        "post_processing_size": null,
        "dataset_size": 10405708,
        "size_in_bytes": 14523709
    },
    "cb": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe CommitmentBank (De Marneffe et al., 2019) is a corpus of short texts in which at least\none sentence contains an embedded clause. Each of these embedded clauses is annotated with the\ndegree to which we expect that the person who wrote the text is committed to the truth of the clause.\nThe resulting task framed as three-class textual entailment on examples that are drawn from the Wall\nStreet Journal, fiction from the British National Corpus, and Switchboard. Each example consists\nof a premise containing an embedded clause and the corresponding hypothesis is the extraction of\nthat clause. We use a subset of the data that had inter-annotator agreement above 0.85. The data is\nimbalanced (relatively fewer neutral examples), so we evaluate using accuracy and F1, where for\nmulti-class F1 we compute the unweighted average of the F1 per class.",
        "citation": "@article{de marneff_simons_tonhauser_2019,\n  title={The CommitmentBank: Investigating projection in naturally occurring discourse},\n  journal={proceedings of Sinn und Bedeutung 23},\n  author={De Marneff, Marie-Catherine and Simons, Mandy and Tonhauser, Judith},\n  year={2019}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://github.com/mcdm/CommitmentBank",
        "license": "",
        "features": {
            "premise": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "hypothesis": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 3,
                "names": [
                    "entailment",
                    "contradiction",
                    "neutral"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "cb",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 93660,
                "num_examples": 250,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 87218,
                "num_examples": 250,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 21894,
                "num_examples": 56,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/CB.zip": {
                "num_bytes": 75482,
                "checksum": "8d641383298d54554066ba1c93f56ae7410af75df621b90c63028806bbbbb535"
            }
        },
        "download_size": 75482,
        "post_processing_size": null,
        "dataset_size": 202772,
        "size_in_bytes": 278254
    },
    "copa": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe Choice Of Plausible Alternatives (COPA, Roemmele et al., 2011) dataset is a causal\nreasoning task in which a system is given a premise sentence and two possible alternatives. The\nsystem must choose the alternative which has the more plausible causal relationship with the premise.\nThe method used for the construction of the alternatives ensures that the task requires causal reasoning\nto solve. Examples either deal with alternative possible causes or alternative possible effects of the\npremise sentence, accompanied by a simple question disambiguating between the two instance\ntypes for the model. All examples are handcrafted and focus on topics from online blogs and a\nphotography-related encyclopedia. Following the recommendation of the authors, we evaluate using\naccuracy.",
        "citation": "@inproceedings{roemmele2011choice,\n  title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},\n  author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},\n  booktitle={2011 AAAI Spring Symposium Series},\n  year={2011}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "http://people.ict.usc.edu/~gordon/copa.html",
        "license": "",
        "features": {
            "premise": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "choice1": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "choice2": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "question": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "choice1",
                    "choice2"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "copa",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 60303,
                "num_examples": 500,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 49599,
                "num_examples": 400,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 12586,
                "num_examples": 100,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/COPA.zip": {
                "num_bytes": 43986,
                "checksum": "405906cddac74bc1e1ce8220f1107d1025b66a25ef10149d91b10bb30651125f"
            }
        },
        "download_size": 43986,
        "post_processing_size": null,
        "dataset_size": 122488,
        "size_in_bytes": 166474
    },
    "multirc": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe Multi-Sentence Reading Comprehension dataset (MultiRC, Khashabi et al., 2018)\nis a true/false question-answering task. Each example consists of a context paragraph, a question\nabout that paragraph, and a list of possible answers to that question which must be labeled as true or\nfalse. Question-answering (QA) is a popular problem with many datasets. We use MultiRC because\nof a number of desirable properties: (i) each question can have multiple possible correct answers,\nso each question-answer pair must be evaluated independent of other pairs, (ii) the questions are\ndesigned such that answering each question requires drawing facts from multiple context sentences,\nand (iii) the question-answer pair format more closely matches the API of other SuperGLUE tasks\nthan span-based extractive QA does. The paragraphs are drawn from seven domains including news,\nfiction, and historical text.",
        "citation": "@inproceedings{MultiRC2018,\n    author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},\n    title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},\n    booktitle = {Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL)},\n    year = {2018}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://cogcomp.org/multirc/",
        "license": "",
        "features": {
            "paragraph": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "question": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "paragraph": {
                    "dtype": "int32",
                    "id": null,
                    "_type": "Value"
                },
                "question": {
                    "dtype": "int32",
                    "id": null,
                    "_type": "Value"
                },
                "answer": {
                    "dtype": "int32",
                    "id": null,
                    "_type": "Value"
                }
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "False",
                    "True"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "multirc",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 14996451,
                "num_examples": 9693,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 46213579,
                "num_examples": 27243,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 7758918,
                "num_examples": 4848,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/MultiRC.zip": {
                "num_bytes": 1116225,
                "checksum": "b3cd440856e72eb166b2edcd37b798455f1ebd51f2c3de64c0c2a4e1971d2737"
            }
        },
        "download_size": 1116225,
        "post_processing_size": null,
        "dataset_size": 68968948,
        "size_in_bytes": 70085173
    },
    "record": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\n(Reading Comprehension with Commonsense Reasoning Dataset, Zhang et al., 2018) is a\nmultiple-choice QA task. Each example consists of a news article and a Cloze-style question about\nthe article in which one entity is masked out. The system must predict the masked out entity from a\ngiven list of possible entities in the provided passage, where the same entity may be expressed using\nmultiple different surface forms, all of which are considered correct. Articles are drawn from CNN\nand Daily Mail. Following the original work, we evaluate with max (over all mentions) token-level\nF1 and exact match (EM).",
        "citation": "@article{zhang2018record,\n  title={Record: Bridging the gap between human and machine commonsense reading comprehension},\n  author={Zhang, Sheng and Liu, Xiaodong and Liu, Jingjing and Gao, Jianfeng and Duh, Kevin and Van Durme, Benjamin},\n  journal={arXiv preprint arXiv:1810.12885},\n  year={2018}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://sheng-z.github.io/ReCoRD-explorer/",
        "license": "",
        "features": {
            "passage": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "query": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "entities": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "entity_spans": {
                "feature": {
                    "text": {
                        "dtype": "string",
                        "id": null,
                        "_type": "Value"
                    },
                    "start": {
                        "dtype": "int32",
                        "id": null,
                        "_type": "Value"
                    },
                    "end": {
                        "dtype": "int32",
                        "id": null,
                        "_type": "Value"
                    }
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "answers": {
                "feature": {
                    "dtype": "string",
                    "id": null,
                    "_type": "Value"
                },
                "length": -1,
                "id": null,
                "_type": "Sequence"
            },
            "idx": {
                "passage": {
                    "dtype": "int32",
                    "id": null,
                    "_type": "Value"
                },
                "query": {
                    "dtype": "int32",
                    "id": null,
                    "_type": "Value"
                }
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "record",
        "version": {
            "version_str": "1.0.3",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 3
        },
        "splits": {
            "train": {
                "name": "train",
                "num_bytes": 179232052,
                "num_examples": 100730,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 17479084,
                "num_examples": 10000,
                "dataset_name": "super_glue"
            },
            "test": {
                "name": "test",
                "num_bytes": 17200575,
                "num_examples": 10000,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/ReCoRD.zip": {
                "num_bytes": 51757880,
                "checksum": "30c7b651ab21b8bf8fab986495cd1084333010e040548f861b839eec0044ac18"
            }
        },
        "download_size": 51757880,
        "post_processing_size": null,
        "dataset_size": 213911711,
        "size_in_bytes": 265669591
    },
    "rte": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe Recognizing Textual Entailment (RTE) datasets come from a series of annual competitions\non textual entailment, the problem of predicting whether a given premise sentence entails a given\nhypothesis sentence (also known as natural language inference, NLI). RTE was previously included\nin GLUE, and we use the same data and format as before: We merge data from RTE1 (Dagan\net al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli\net al., 2009). All datasets are combined and converted to two-class classification: entailment and\nnot_entailment. Of all the GLUE tasks, RTE was among those that benefited from transfer learning\nthe most, jumping from near random-chance performance (~56%) at the time of GLUE's launch to\n85% accuracy (Liu et al., 2019c) at the time of writing. Given the eight point gap with respect to\nhuman performance, however, the task is not yet solved by machines, and we expect the remaining\ngap to be difficult to close.",
        "citation": "@inproceedings{dagan2005pascal,\n  title={The PASCAL recognising textual entailment challenge},\n  author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},\n  booktitle={Machine Learning Challenges Workshop},\n  pages={177--190},\n  year={2005},\n  organization={Springer}\n}\n@inproceedings{bar2006second,\n  title={The second pascal recognising textual entailment challenge},\n  author={Bar-Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},\n  booktitle={Proceedings of the second PASCAL challenges workshop on recognising textual entailment},\n  volume={6},\n  number={1},\n  pages={6--4},\n  year={2006},\n  organization={Venice}\n}\n@inproceedings{giampiccolo2007third,\n  title={The third pascal recognizing textual entailment challenge},\n  author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},\n  booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},\n  pages={1--9},\n  year={2007},\n  organization={Association for Computational Linguistics}\n}\n@inproceedings{bentivogli2009fifth,\n  title={The Fifth PASCAL Recognizing Textual Entailment Challenge.},\n  author={Bentivogli, Luisa and Clark, Peter and Dagan, Ido and Giampiccolo, Danilo},\n  booktitle={TAC},\n  year={2009}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://aclweb.org/aclwiki/Recognizing_Textual_Entailment",
        "license": "",
        "features": {
            "premise": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "hypothesis": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "entailment",
                    "not_entailment"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "rte",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 975799,
                "num_examples": 3000,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 848745,
                "num_examples": 2490,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 90899,
                "num_examples": 277,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/RTE.zip": {
                "num_bytes": 750920,
                "checksum": "6310aab3f000424c9d0318a1ff20692e07c7f4aa15e8f17a5972ea0a35c398b9"
            }
        },
        "download_size": 750920,
        "post_processing_size": null,
        "dataset_size": 1915443,
        "size_in_bytes": 2666363
    },
    "wic": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe Word-in-Context (WiC, Pilehvar and Camacho-Collados, 2019) dataset supports a word\nsense disambiguation task cast as binary classification over sentence pairs. Given two sentences and a\npolysemous (sense-ambiguous) word that appears in both sentences, the task is to determine whether\nthe word is used with the same sense in both sentences. Sentences are drawn from WordNet (Miller,\n1995), VerbNet (Schuler, 2005), and Wiktionary. We follow the original work and evaluate using\naccuracy.",
        "citation": "@article{DBLP:journals/corr/abs-1808-09121,\n  author={Mohammad Taher Pilehvar and os{'{e}} Camacho{-}Collados},\n  title={WiC: 10, 000 Example Pairs for Evaluating Context-Sensitive Representations},\n  journal={CoRR},\n  volume={abs/1808.09121},\n  year={2018},\n  url={http://arxiv.org/abs/1808.09121},\n  archivePrefix={arXiv},\n  eprint={1808.09121},\n  timestamp={Mon, 03 Sep 2018 13:36:40 +0200},\n  biburl={https://dblp.org/rec/bib/journals/corr/abs-1808-09121},\n  bibsource={dblp computer science bibliography, https://dblp.org}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://pilehvar.github.io/wic/",
        "license": "",
        "features": {
            "word": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "sentence1": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "sentence2": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "start1": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "start2": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "end1": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "end2": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "False",
                    "True"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "wic",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 180593,
                "num_examples": 1400,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 665183,
                "num_examples": 5428,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 82623,
                "num_examples": 638,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WiC.zip": {
                "num_bytes": 396213,
                "checksum": "ee7e67f4ae9eafbf533780faa198e62167f3cda54256cdf261877be3c0e90900"
            }
        },
        "download_size": 396213,
        "post_processing_size": null,
        "dataset_size": 928399,
        "size_in_bytes": 1324612
    },
    "wsc": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe Winograd Schema Challenge (WSC, Levesque et al., 2012) is a reading comprehension\ntask in which a system must read a sentence with a pronoun and select the referent of that pronoun\nfrom a list of choices. Given the difficulty of this task and the headroom still left, we have included\nWSC in SuperGLUE and recast the dataset into its coreference form. The task is cast as a binary\nclassification problem, as opposed to N-multiple choice, in order to isolate the model's ability to\nunderstand the coreference links within a sentence as opposed to various other strategies that may\ncome into play in multiple choice conditions. With that in mind, we create a split with 65% negative\nmajority class in the validation set, reflecting the distribution of the hidden test set, and 52% negative\nclass in the training set. The training and validation examples are drawn from the original Winograd\nSchema dataset (Levesque et al., 2012), as well as those distributed by the affiliated organization\nCommonsense Reasoning. The test examples are derived from fiction books and have been shared\nwith us by the authors of the original dataset. Previously, a version of WSC recast as NLI as included\nin GLUE, known as WNLI. No substantial progress was made on WNLI, with many submissions\nopting to submit only majority class predictions. WNLI was made especially difficult due to an\nadversarial train/dev split: Premise sentences that appeared in the training set sometimes appeared\nin the development set with a different hypothesis and a flipped label. If a system memorized the\ntraining set without meaningfully generalizing, which was easy due to the small size of the training\nset, it could perform far below chance on the development set. We remove this adversarial design\nin the SuperGLUE version of WSC by ensuring that no sentences are shared between the training,\nvalidation, and test sets.\n\nHowever, the validation and test sets come from different domains, with the validation set consisting\nof ambiguous examples such that changing one non-noun phrase word will change the coreference\ndependencies in the sentence. The test set consists only of more straightforward examples, with a\nhigh number of noun phrases (and thus more choices for the model), but low to no ambiguity.",
        "citation": "@inproceedings{levesque2012winograd,\n  title={The winograd schema challenge},\n  author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},\n  booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},\n  year={2012}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html",
        "license": "",
        "features": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "span1_index": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "span2_index": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "span1_text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "span2_text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "False",
                    "True"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "wsc",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 31572,
                "num_examples": 146,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 89883,
                "num_examples": 554,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 21637,
                "num_examples": 104,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WSC.zip": {
                "num_bytes": 32751,
                "checksum": "2ed6dfa94556b4a128ff0441efe365b2e883124e7e6aa00fb8d3a6cb1fd520a9"
            }
        },
        "download_size": 32751,
        "post_processing_size": null,
        "dataset_size": 143092,
        "size_in_bytes": 175843
    },
    "wsc.fixed": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nThe Winograd Schema Challenge (WSC, Levesque et al., 2012) is a reading comprehension\ntask in which a system must read a sentence with a pronoun and select the referent of that pronoun\nfrom a list of choices. Given the difficulty of this task and the headroom still left, we have included\nWSC in SuperGLUE and recast the dataset into its coreference form. The task is cast as a binary\nclassification problem, as opposed to N-multiple choice, in order to isolate the model's ability to\nunderstand the coreference links within a sentence as opposed to various other strategies that may\ncome into play in multiple choice conditions. With that in mind, we create a split with 65% negative\nmajority class in the validation set, reflecting the distribution of the hidden test set, and 52% negative\nclass in the training set. The training and validation examples are drawn from the original Winograd\nSchema dataset (Levesque et al., 2012), as well as those distributed by the affiliated organization\nCommonsense Reasoning. The test examples are derived from fiction books and have been shared\nwith us by the authors of the original dataset. Previously, a version of WSC recast as NLI as included\nin GLUE, known as WNLI. No substantial progress was made on WNLI, with many submissions\nopting to submit only majority class predictions. WNLI was made especially difficult due to an\nadversarial train/dev split: Premise sentences that appeared in the training set sometimes appeared\nin the development set with a different hypothesis and a flipped label. If a system memorized the\ntraining set without meaningfully generalizing, which was easy due to the small size of the training\nset, it could perform far below chance on the development set. We remove this adversarial design\nin the SuperGLUE version of WSC by ensuring that no sentences are shared between the training,\nvalidation, and test sets.\n\nHowever, the validation and test sets come from different domains, with the validation set consisting\nof ambiguous examples such that changing one non-noun phrase word will change the coreference\ndependencies in the sentence. The test set consists only of more straightforward examples, with a\nhigh number of noun phrases (and thus more choices for the model), but low to no ambiguity.\n\nThis version fixes issues where the spans are not actually substrings of the text.",
        "citation": "@inproceedings{levesque2012winograd,\n  title={The winograd schema challenge},\n  author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},\n  booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},\n  year={2012}\n}\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html",
        "license": "",
        "features": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "span1_index": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "span2_index": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "span1_text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "span2_text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "False",
                    "True"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "wsc.fixed",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 31568,
                "num_examples": 146,
                "dataset_name": "super_glue"
            },
            "train": {
                "name": "train",
                "num_bytes": 89883,
                "num_examples": 554,
                "dataset_name": "super_glue"
            },
            "validation": {
                "name": "validation",
                "num_bytes": 21637,
                "num_examples": 104,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WSC.zip": {
                "num_bytes": 32751,
                "checksum": "2ed6dfa94556b4a128ff0441efe365b2e883124e7e6aa00fb8d3a6cb1fd520a9"
            }
        },
        "download_size": 32751,
        "post_processing_size": null,
        "dataset_size": 143088,
        "size_in_bytes": 175839
    },
    "axb": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nAn expert-constructed,\ndiagnostic dataset that automatically tests models for a broad range of linguistic, commonsense, and\nworld knowledge. Each example in this broad-coverage diagnostic is a sentence pair labeled with\na three-way entailment relation (entailment, neutral, or contradiction) and tagged with labels that\nindicate the phenomena that characterize the relationship between the two sentences. Submissions\nto the GLUE leaderboard are required to include predictions from the submission's MultiNLI\nclassifier on the diagnostic dataset, and analyses of the results were shown alongside the main\nleaderboard. Since this broad-coverage diagnostic task has proved difficult for top models, we retain\nit in SuperGLUE. However, since MultiNLI is not part of SuperGLUE, we collapse contradiction\nand neutral into a single not_entailment label, and request that submissions include predictions\non the resulting set from the model used for the RTE task.\n",
        "citation": "\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://gluebenchmark.com/diagnostics",
        "license": "",
        "features": {
            "sentence1": {
                "dtype": "string",
                "_type": "Value"
            },
            "sentence2": {
                "dtype": "string",
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "_type": "Value"
            },
            "label": {
                "names": [
                    "entailment",
                    "not_entailment"
                ],
                "_type": "ClassLabel"
            }
        },
        "builder_name": "parquet",
        "dataset_name": "super_glue",
        "config_name": "axb",
        "version": {
            "version_str": "1.0.3",
            "major": 1,
            "minor": 0,
            "patch": 3
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 237694,
                "num_examples": 1104,
                "dataset_name": null
            }
        },
        "download_size": 80924,
        "dataset_size": 237694,
        "size_in_bytes": 318618
    },
    "axg": {
        "description": "SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n\nWinogender is designed to measure gender\nbias in coreference resolution systems. We use the Diverse Natural Language Inference Collection\n(DNC; Poliak et al., 2018) version that casts Winogender as a textual entailment task. Each example\nconsists of a premise sentence with a male or female pronoun and a hypothesis giving a possible\nantecedent of the pronoun. Examples occur in minimal pairs, where the only difference between\nan example and its pair is the gender of the pronoun in the premise. Performance on Winogender\nis measured with both accuracy and the gender parity score: the percentage of minimal pairs for\nwhich the predictions are the same. We note that a system can trivially obtain a perfect gender parity\nscore by guessing the same class for all examples, so a high gender parity score is meaningless unless\naccompanied by high accuracy. As a diagnostic test of gender bias, we view the schemas as having high\npositive predictive value and low negative predictive value; that is, they may demonstrate the presence\nof gender bias in a system, but not prove its absence.\n",
        "citation": "@inproceedings{rudinger-EtAl:2018:N18,\n  author    = {Rudinger, Rachel  and  Naradowsky, Jason  and  Leonard, Brian  and  {Van Durme}, Benjamin},\n  title     = {Gender Bias in Coreference Resolution},\n  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},\n  month     = {June},\n  year      = {2018},\n  address   = {New Orleans, Louisiana},\n  publisher = {Association for Computational Linguistics}\n}\n\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n\nNote that each SuperGLUE dataset has its own citation. Please see the source to\nget the correct citation for each contained dataset.\n",
        "homepage": "https://github.com/rudinger/winogender-schemas",
        "license": "",
        "features": {
            "premise": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "hypothesis": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "idx": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "label": {
                "num_classes": 2,
                "names": [
                    "entailment",
                    "not_entailment"
                ],
                "id": null,
                "_type": "ClassLabel"
            }
        },
        "post_processed": null,
        "supervised_keys": null,
        "task_templates": null,
        "builder_name": "super_glue",
        "config_name": "axg",
        "version": {
            "version_str": "1.0.2",
            "description": null,
            "major": 1,
            "minor": 0,
            "patch": 2
        },
        "splits": {
            "test": {
                "name": "test",
                "num_bytes": 53581,
                "num_examples": 356,
                "dataset_name": "super_glue"
            }
        },
        "download_checksums": {
            "https://dl.fbaipublicfiles.com/glue/superglue/data/v2/AX-g.zip": {
                "num_bytes": 10413,
                "checksum": "2d4e00d3a7d23d2c3787ee4c1382cc81a72cb05a76fc9d78d142949247ed61b9"
            }
        },
        "download_size": 10413,
        "post_processing_size": null,
        "dataset_size": 53581,
        "size_in_bytes": 63994
    }
}