Hugging Face's logo

Datasets:
nyu-mll
/
glue

Tasks:
Text Classification
Modalities:
Tabular
Text
Formats:
parquet
Sub-tasks:
acceptability-classification
natural-language-inference
semantic-similarity-scoring
Languages:
English
Size:
1M - 10M
ArXiv:
Tags:
qa-nli
coreference-nli
paraphrase-identification
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
24
albertvillanova HF staff commited on
Commit
0de2392
•
1 Parent(s): 11762d9

Delete legacy dataset_infos.json

Browse files
Files changed (1) hide show
  1. dataset_infos.json +0 -703
dataset_infos.json DELETED
@@ -1,703 +0,0 @@
1
- {
2
- "cola": {
3
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
4
- "citation": "@article{warstadt2018neural,\n title={Neural Network Acceptability Judgments},\n author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1805.12471},\n year={2018}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
5
- "homepage": "https://nyu-mll.github.io/CoLA/",
6
- "license": "",
7
- "features": {
8
- "sentence": {
9
- "dtype": "string",
10
- "_type": "Value"
11
- },
12
- "label": {
13
- "names": [
14
- "unacceptable",
15
- "acceptable"
16
- ],
17
- "_type": "ClassLabel"
18
- },
19
- "idx": {
20
- "dtype": "int32",
21
- "_type": "Value"
22
- }
23
- },
24
- "builder_name": "glue",
25
- "dataset_name": "glue",
26
- "config_name": "cola",
27
- "version": {
28
- "version_str": "1.0.0",
29
- "description": "",
30
- "major": 1,
31
- "minor": 0,
32
- "patch": 0
33
- },
34
- "splits": {
35
- "train": {
36
- "name": "train",
37
- "num_bytes": 484869,
38
- "num_examples": 8551,
39
- "dataset_name": null
40
- },
41
- "validation": {
42
- "name": "validation",
43
- "num_bytes": 60322,
44
- "num_examples": 1043,
45
- "dataset_name": null
46
- },
47
- "test": {
48
- "name": "test",
49
- "num_bytes": 60513,
50
- "num_examples": 1063,
51
- "dataset_name": null
52
- }
53
- },
54
- "download_size": 326394,
55
- "dataset_size": 605704,
56
- "size_in_bytes": 932098
57
- },
58
- "sst2": {
59
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
60
- "citation": "@inproceedings{socher2013recursive,\n title={Recursive deep models for semantic compositionality over a sentiment treebank},\n author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},\n booktitle={Proceedings of the 2013 conference on empirical methods in natural language processing},\n pages={1631--1642},\n year={2013}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
61
- "homepage": "https://datasets.stanford.edu/sentiment/index.html",
62
- "license": "",
63
- "features": {
64
- "sentence": {
65
- "dtype": "string",
66
- "_type": "Value"
67
- },
68
- "label": {
69
- "names": [
70
- "negative",
71
- "positive"
72
- ],
73
- "_type": "ClassLabel"
74
- },
75
- "idx": {
76
- "dtype": "int32",
77
- "_type": "Value"
78
- }
79
- },
80
- "builder_name": "glue",
81
- "dataset_name": "glue",
82
- "config_name": "sst2",
83
- "version": {
84
- "version_str": "1.0.0",
85
- "description": "",
86
- "major": 1,
87
- "minor": 0,
88
- "patch": 0
89
- },
90
- "splits": {
91
- "train": {
92
- "name": "train",
93
- "num_bytes": 4681603,
94
- "num_examples": 67349,
95
- "dataset_name": null
96
- },
97
- "validation": {
98
- "name": "validation",
99
- "num_bytes": 106252,
100
- "num_examples": 872,
101
- "dataset_name": null
102
- },
103
- "test": {
104
- "name": "test",
105
- "num_bytes": 216640,
106
- "num_examples": 1821,
107
- "dataset_name": null
108
- }
109
- },
110
- "download_size": 3331080,
111
- "dataset_size": 5004495,
112
- "size_in_bytes": 8335575
113
- },
114
- "mrpc": {
115
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
116
- "citation": "@inproceedings{dolan2005automatically,\n title={Automatically constructing a corpus of sentential paraphrases},\n author={Dolan, William B and Brockett, Chris},\n booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n year={2005}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
117
- "homepage": "https://www.microsoft.com/en-us/download/details.aspx?id=52398",
118
- "license": "",
119
- "features": {
120
- "sentence1": {
121
- "dtype": "string",
122
- "_type": "Value"
123
- },
124
- "sentence2": {
125
- "dtype": "string",
126
- "_type": "Value"
127
- },
128
- "label": {
129
- "names": [
130
- "not_equivalent",
131
- "equivalent"
132
- ],
133
- "_type": "ClassLabel"
134
- },
135
- "idx": {
136
- "dtype": "int32",
137
- "_type": "Value"
138
- }
139
- },
140
- "builder_name": "glue",
141
- "dataset_name": "glue",
142
- "config_name": "mrpc",
143
- "version": {
144
- "version_str": "1.0.0",
145
- "description": "",
146
- "major": 1,
147
- "minor": 0,
148
- "patch": 0
149
- },
150
- "splits": {
151
- "train": {
152
- "name": "train",
153
- "num_bytes": 943843,
154
- "num_examples": 3668,
155
- "dataset_name": null
156
- },
157
- "validation": {
158
- "name": "validation",
159
- "num_bytes": 105879,
160
- "num_examples": 408,
161
- "dataset_name": null
162
- },
163
- "test": {
164
- "name": "test",
165
- "num_bytes": 442410,
166
- "num_examples": 1725,
167
- "dataset_name": null
168
- }
169
- },
170
- "download_size": 1033400,
171
- "dataset_size": 1492132,
172
- "size_in_bytes": 2525532
173
- },
174
- "qqp": {
175
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
176
- "citation": "@online{WinNT,\n author = {Iyer, Shankar and Dandekar, Nikhil and Csernai, Kornel},\n title = {First Quora Dataset Release: Question Pairs},\n year = {2017},\n url = {https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs},\n urldate = {2019-04-03}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
177
- "homepage": "https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs",
178
- "license": "",
179
- "features": {
180
- "question1": {
181
- "dtype": "string",
182
- "_type": "Value"
183
- },
184
- "question2": {
185
- "dtype": "string",
186
- "_type": "Value"
187
- },
188
- "label": {
189
- "names": [
190
- "not_duplicate",
191
- "duplicate"
192
- ],
193
- "_type": "ClassLabel"
194
- },
195
- "idx": {
196
- "dtype": "int32",
197
- "_type": "Value"
198
- }
199
- },
200
- "builder_name": "glue",
201
- "dataset_name": "glue",
202
- "config_name": "qqp",
203
- "version": {
204
- "version_str": "1.0.0",
205
- "description": "",
206
- "major": 1,
207
- "minor": 0,
208
- "patch": 0
209
- },
210
- "splits": {
211
- "train": {
212
- "name": "train",
213
- "num_bytes": 50900820,
214
- "num_examples": 363846,
215
- "dataset_name": null
216
- },
217
- "validation": {
218
- "name": "validation",
219
- "num_bytes": 5653754,
220
- "num_examples": 40430,
221
- "dataset_name": null
222
- },
223
- "test": {
224
- "name": "test",
225
- "num_bytes": 55171111,
226
- "num_examples": 390965,
227
- "dataset_name": null
228
- }
229
- },
230
- "download_size": 73982265,
231
- "dataset_size": 111725685,
232
- "size_in_bytes": 185707950
233
- },
234
- "stsb": {
235
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
236
- "citation": "@article{cer2017semeval,\n title={Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation},\n author={Cer, Daniel and Diab, Mona and Agirre, Eneko and Lopez-Gazpio, Inigo and Specia, Lucia},\n journal={arXiv preprint arXiv:1708.00055},\n year={2017}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
237
- "homepage": "http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark",
238
- "license": "",
239
- "features": {
240
- "sentence1": {
241
- "dtype": "string",
242
- "_type": "Value"
243
- },
244
- "sentence2": {
245
- "dtype": "string",
246
- "_type": "Value"
247
- },
248
- "label": {
249
- "dtype": "float32",
250
- "_type": "Value"
251
- },
252
- "idx": {
253
- "dtype": "int32",
254
- "_type": "Value"
255
- }
256
- },
257
- "builder_name": "glue",
258
- "dataset_name": "glue",
259
- "config_name": "stsb",
260
- "version": {
261
- "version_str": "1.0.0",
262
- "description": "",
263
- "major": 1,
264
- "minor": 0,
265
- "patch": 0
266
- },
267
- "splits": {
268
- "train": {
269
- "name": "train",
270
- "num_bytes": 754791,
271
- "num_examples": 5749,
272
- "dataset_name": null
273
- },
274
- "validation": {
275
- "name": "validation",
276
- "num_bytes": 216064,
277
- "num_examples": 1500,
278
- "dataset_name": null
279
- },
280
- "test": {
281
- "name": "test",
282
- "num_bytes": 169974,
283
- "num_examples": 1379,
284
- "dataset_name": null
285
- }
286
- },
287
- "download_size": 766983,
288
- "dataset_size": 1140829,
289
- "size_in_bytes": 1907812
290
- },
291
- "mnli": {
292
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
293
- "citation": "@InProceedings{N18-1101,\n author = \"Williams, Adina\n and Nangia, Nikita\n and Bowman, Samuel\",\n title = \"A Broad-Coverage Challenge Corpus for\n Sentence Understanding through Inference\",\n booktitle = \"Proceedings of the 2018 Conference of\n the North American Chapter of the\n Association for Computational Linguistics:\n Human Language Technologies, Volume 1 (Long\n Papers)\",\n year = \"2018\",\n publisher = \"Association for Computational Linguistics\",\n pages = \"1112--1122\",\n location = \"New Orleans, Louisiana\",\n url = \"http://aclweb.org/anthology/N18-1101\"\n}\n@article{bowman2015large,\n title={A large annotated corpus for learning natural language inference},\n author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},\n journal={arXiv preprint arXiv:1508.05326},\n year={2015}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
294
- "homepage": "http://www.nyu.edu/projects/bowman/multinli/",
295
- "license": "",
296
- "features": {
297
- "premise": {
298
- "dtype": "string",
299
- "_type": "Value"
300
- },
301
- "hypothesis": {
302
- "dtype": "string",
303
- "_type": "Value"
304
- },
305
- "label": {
306
- "names": [
307
- "entailment",
308
- "neutral",
309
- "contradiction"
310
- ],
311
- "_type": "ClassLabel"
312
- },
313
- "idx": {
314
- "dtype": "int32",
315
- "_type": "Value"
316
- }
317
- },
318
- "builder_name": "glue",
319
- "dataset_name": "glue",
320
- "config_name": "mnli",
321
- "version": {
322
- "version_str": "1.0.0",
323
- "description": "",
324
- "major": 1,
325
- "minor": 0,
326
- "patch": 0
327
- },
328
- "splits": {
329
- "train": {
330
- "name": "train",
331
- "num_bytes": 74619646,
332
- "num_examples": 392702,
333
- "dataset_name": null
334
- },
335
- "validation_matched": {
336
- "name": "validation_matched",
337
- "num_bytes": 1833783,
338
- "num_examples": 9815,
339
- "dataset_name": null
340
- },
341
- "validation_mismatched": {
342
- "name": "validation_mismatched",
343
- "num_bytes": 1949231,
344
- "num_examples": 9832,
345
- "dataset_name": null
346
- },
347
- "test_matched": {
348
- "name": "test_matched",
349
- "num_bytes": 1848654,
350
- "num_examples": 9796,
351
- "dataset_name": null
352
- },
353
- "test_mismatched": {
354
- "name": "test_mismatched",
355
- "num_bytes": 1950703,
356
- "num_examples": 9847,
357
- "dataset_name": null
358
- }
359
- },
360
- "download_size": 57168425,
361
- "dataset_size": 82202017,
362
- "size_in_bytes": 139370442
363
- },
364
- "mnli_mismatched": {
365
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
366
- "citation": "@InProceedings{N18-1101,\n author = \"Williams, Adina\n and Nangia, Nikita\n and Bowman, Samuel\",\n title = \"A Broad-Coverage Challenge Corpus for\n Sentence Understanding through Inference\",\n booktitle = \"Proceedings of the 2018 Conference of\n the North American Chapter of the\n Association for Computational Linguistics:\n Human Language Technologies, Volume 1 (Long\n Papers)\",\n year = \"2018\",\n publisher = \"Association for Computational Linguistics\",\n pages = \"1112--1122\",\n location = \"New Orleans, Louisiana\",\n url = \"http://aclweb.org/anthology/N18-1101\"\n}\n@article{bowman2015large,\n title={A large annotated corpus for learning natural language inference},\n author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},\n journal={arXiv preprint arXiv:1508.05326},\n year={2015}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
367
- "homepage": "http://www.nyu.edu/projects/bowman/multinli/",
368
- "license": "",
369
- "features": {
370
- "premise": {
371
- "dtype": "string",
372
- "_type": "Value"
373
- },
374
- "hypothesis": {
375
- "dtype": "string",
376
- "_type": "Value"
377
- },
378
- "label": {
379
- "names": [
380
- "entailment",
381
- "neutral",
382
- "contradiction"
383
- ],
384
- "_type": "ClassLabel"
385
- },
386
- "idx": {
387
- "dtype": "int32",
388
- "_type": "Value"
389
- }
390
- },
391
- "builder_name": "glue",
392
- "dataset_name": "glue",
393
- "config_name": "mnli_mismatched",
394
- "version": {
395
- "version_str": "1.0.0",
396
- "description": "",
397
- "major": 1,
398
- "minor": 0,
399
- "patch": 0
400
- },
401
- "splits": {
402
- "validation": {
403
- "name": "validation",
404
- "num_bytes": 1949231,
405
- "num_examples": 9832,
406
- "dataset_name": null
407
- },
408
- "test": {
409
- "name": "test",
410
- "num_bytes": 1950703,
411
- "num_examples": 9847,
412
- "dataset_name": null
413
- }
414
- },
415
- "download_size": 2509009,
416
- "dataset_size": 3899934,
417
- "size_in_bytes": 6408943
418
- },
419
- "mnli_matched": {
420
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
421
- "citation": "@InProceedings{N18-1101,\n author = \"Williams, Adina\n and Nangia, Nikita\n and Bowman, Samuel\",\n title = \"A Broad-Coverage Challenge Corpus for\n Sentence Understanding through Inference\",\n booktitle = \"Proceedings of the 2018 Conference of\n the North American Chapter of the\n Association for Computational Linguistics:\n Human Language Technologies, Volume 1 (Long\n Papers)\",\n year = \"2018\",\n publisher = \"Association for Computational Linguistics\",\n pages = \"1112--1122\",\n location = \"New Orleans, Louisiana\",\n url = \"http://aclweb.org/anthology/N18-1101\"\n}\n@article{bowman2015large,\n title={A large annotated corpus for learning natural language inference},\n author={Bowman, Samuel R and Angeli, Gabor and Potts, Christopher and Manning, Christopher D},\n journal={arXiv preprint arXiv:1508.05326},\n year={2015}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
422
- "homepage": "http://www.nyu.edu/projects/bowman/multinli/",
423
- "license": "",
424
- "features": {
425
- "premise": {
426
- "dtype": "string",
427
- "_type": "Value"
428
- },
429
- "hypothesis": {
430
- "dtype": "string",
431
- "_type": "Value"
432
- },
433
- "label": {
434
- "names": [
435
- "entailment",
436
- "neutral",
437
- "contradiction"
438
- ],
439
- "_type": "ClassLabel"
440
- },
441
- "idx": {
442
- "dtype": "int32",
443
- "_type": "Value"
444
- }
445
- },
446
- "builder_name": "glue",
447
- "dataset_name": "glue",
448
- "config_name": "mnli_matched",
449
- "version": {
450
- "version_str": "1.0.0",
451
- "description": "",
452
- "major": 1,
453
- "minor": 0,
454
- "patch": 0
455
- },
456
- "splits": {
457
- "validation": {
458
- "name": "validation",
459
- "num_bytes": 1833783,
460
- "num_examples": 9815,
461
- "dataset_name": null
462
- },
463
- "test": {
464
- "name": "test",
465
- "num_bytes": 1848654,
466
- "num_examples": 9796,
467
- "dataset_name": null
468
- }
469
- },
470
- "download_size": 2435055,
471
- "dataset_size": 3682437,
472
- "size_in_bytes": 6117492
473
- },
474
- "qnli": {
475
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
476
- "citation": "@article{rajpurkar2016squad,\n title={Squad: 100,000+ questions for machine comprehension of text},\n author={Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy},\n journal={arXiv preprint arXiv:1606.05250},\n year={2016}\n}\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
477
- "homepage": "https://rajpurkar.github.io/SQuAD-explorer/",
478
- "license": "",
479
- "features": {
480
- "question": {
481
- "dtype": "string",
482
- "_type": "Value"
483
- },
484
- "sentence": {
485
- "dtype": "string",
486
- "_type": "Value"
487
- },
488
- "label": {
489
- "names": [
490
- "entailment",
491
- "not_entailment"
492
- ],
493
- "_type": "ClassLabel"
494
- },
495
- "idx": {
496
- "dtype": "int32",
497
- "_type": "Value"
498
- }
499
- },
500
- "builder_name": "glue",
501
- "dataset_name": "glue",
502
- "config_name": "qnli",
503
- "version": {
504
- "version_str": "1.0.0",
505
- "description": "",
506
- "major": 1,
507
- "minor": 0,
508
- "patch": 0
509
- },
510
- "splits": {
511
- "train": {
512
- "name": "train",
513
- "num_bytes": 25612443,
514
- "num_examples": 104743,
515
- "dataset_name": null
516
- },
517
- "validation": {
518
- "name": "validation",
519
- "num_bytes": 1368304,
520
- "num_examples": 5463,
521
- "dataset_name": null
522
- },
523
- "test": {
524
- "name": "test",
525
- "num_bytes": 1373093,
526
- "num_examples": 5463,
527
- "dataset_name": null
528
- }
529
- },
530
- "download_size": 19278324,
531
- "dataset_size": 28353840,
532
- "size_in_bytes": 47632164
533
- },
534
- "rte": {
535
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
536
- "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@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
537
- "homepage": "https://aclweb.org/aclwiki/Recognizing_Textual_Entailment",
538
- "license": "",
539
- "features": {
540
- "sentence1": {
541
- "dtype": "string",
542
- "_type": "Value"
543
- },
544
- "sentence2": {
545
- "dtype": "string",
546
- "_type": "Value"
547
- },
548
- "label": {
549
- "names": [
550
- "entailment",
551
- "not_entailment"
552
- ],
553
- "_type": "ClassLabel"
554
- },
555
- "idx": {
556
- "dtype": "int32",
557
- "_type": "Value"
558
- }
559
- },
560
- "builder_name": "glue",
561
- "dataset_name": "glue",
562
- "config_name": "rte",
563
- "version": {
564
- "version_str": "1.0.0",
565
- "description": "",
566
- "major": 1,
567
- "minor": 0,
568
- "patch": 0
569
- },
570
- "splits": {
571
- "train": {
572
- "name": "train",
573
- "num_bytes": 847320,
574
- "num_examples": 2490,
575
- "dataset_name": null
576
- },
577
- "validation": {
578
- "name": "validation",
579
- "num_bytes": 90728,
580
- "num_examples": 277,
581
- "dataset_name": null
582
- },
583
- "test": {
584
- "name": "test",
585
- "num_bytes": 974053,
586
- "num_examples": 3000,
587
- "dataset_name": null
588
- }
589
- },
590
- "download_size": 1274409,
591
- "dataset_size": 1912101,
592
- "size_in_bytes": 3186510
593
- },
594
- "wnli": {
595
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
596
- "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@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
597
- "homepage": "https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html",
598
- "license": "",
599
- "features": {
600
- "sentence1": {
601
- "dtype": "string",
602
- "_type": "Value"
603
- },
604
- "sentence2": {
605
- "dtype": "string",
606
- "_type": "Value"
607
- },
608
- "label": {
609
- "names": [
610
- "not_entailment",
611
- "entailment"
612
- ],
613
- "_type": "ClassLabel"
614
- },
615
- "idx": {
616
- "dtype": "int32",
617
- "_type": "Value"
618
- }
619
- },
620
- "builder_name": "glue",
621
- "dataset_name": "glue",
622
- "config_name": "wnli",
623
- "version": {
624
- "version_str": "1.0.0",
625
- "description": "",
626
- "major": 1,
627
- "minor": 0,
628
- "patch": 0
629
- },
630
- "splits": {
631
- "train": {
632
- "name": "train",
633
- "num_bytes": 107109,
634
- "num_examples": 635,
635
- "dataset_name": null
636
- },
637
- "validation": {
638
- "name": "validation",
639
- "num_bytes": 12162,
640
- "num_examples": 71,
641
- "dataset_name": null
642
- },
643
- "test": {
644
- "name": "test",
645
- "num_bytes": 37889,
646
- "num_examples": 146,
647
- "dataset_name": null
648
- }
649
- },
650
- "download_size": 63522,
651
- "dataset_size": 157160,
652
- "size_in_bytes": 220682
653
- },
654
- "ax": {
655
- "description": "GLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n\n",
656
- "citation": "\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n",
657
- "homepage": "https://gluebenchmark.com/diagnostics",
658
- "license": "",
659
- "features": {
660
- "premise": {
661
- "dtype": "string",
662
- "_type": "Value"
663
- },
664
- "hypothesis": {
665
- "dtype": "string",
666
- "_type": "Value"
667
- },
668
- "label": {
669
- "names": [
670
- "entailment",
671
- "neutral",
672
- "contradiction"
673
- ],
674
- "_type": "ClassLabel"
675
- },
676
- "idx": {
677
- "dtype": "int32",
678
- "_type": "Value"
679
- }
680
- },
681
- "builder_name": "glue",
682
- "dataset_name": "glue",
683
- "config_name": "ax",
684
- "version": {
685
- "version_str": "1.0.0",
686
- "description": "",
687
- "major": 1,
688
- "minor": 0,
689
- "patch": 0
690
- },
691
- "splits": {
692
- "test": {
693
- "name": "test",
694
- "num_bytes": 237694,
695
- "num_examples": 1104,
696
- "dataset_name": null
697
- }
698
- },
699
- "download_size": 80767,
700
- "dataset_size": 237694,
701
- "size_in_bytes": 318461
702
- }
703
- }