Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
machine-generated
Annotations Creators:
other
Source Datasets:
extended|glue
ArXiv:
License:
albertvillanova HF staff commited on
Commit
340a2fb
1 Parent(s): ca822b4

Delete legacy dataset_infos.json

Browse files
Files changed (1) hide show
  1. dataset_infos.json +0 -283
dataset_infos.json DELETED
@@ -1,283 +0,0 @@
1
- {
2
- "adv_sst2": {
3
- "description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n",
4
- "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n",
5
- "homepage": "https://adversarialglue.github.io/",
6
- "license": "",
7
- "features": {
8
- "sentence": {
9
- "dtype": "string",
10
- "_type": "Value"
11
- },
12
- "label": {
13
- "names": [
14
- "negative",
15
- "positive"
16
- ],
17
- "_type": "ClassLabel"
18
- },
19
- "idx": {
20
- "dtype": "int32",
21
- "_type": "Value"
22
- }
23
- },
24
- "builder_name": "parquet",
25
- "dataset_name": "adv_glue",
26
- "config_name": "adv_sst2",
27
- "version": {
28
- "version_str": "1.0.0",
29
- "major": 1,
30
- "minor": 0,
31
- "patch": 0
32
- },
33
- "splits": {
34
- "validation": {
35
- "name": "validation",
36
- "num_bytes": 16572,
37
- "num_examples": 148,
38
- "dataset_name": null
39
- }
40
- },
41
- "download_size": 10833,
42
- "dataset_size": 16572,
43
- "size_in_bytes": 27405
44
- },
45
- "adv_qqp": {
46
- "description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n",
47
- "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n",
48
- "homepage": "https://adversarialglue.github.io/",
49
- "license": "",
50
- "features": {
51
- "question1": {
52
- "dtype": "string",
53
- "_type": "Value"
54
- },
55
- "question2": {
56
- "dtype": "string",
57
- "_type": "Value"
58
- },
59
- "label": {
60
- "names": [
61
- "not_duplicate",
62
- "duplicate"
63
- ],
64
- "_type": "ClassLabel"
65
- },
66
- "idx": {
67
- "dtype": "int32",
68
- "_type": "Value"
69
- }
70
- },
71
- "builder_name": "parquet",
72
- "dataset_name": "adv_glue",
73
- "config_name": "adv_qqp",
74
- "version": {
75
- "version_str": "1.0.0",
76
- "major": 1,
77
- "minor": 0,
78
- "patch": 0
79
- },
80
- "splits": {
81
- "validation": {
82
- "name": "validation",
83
- "num_bytes": 9908,
84
- "num_examples": 78,
85
- "dataset_name": null
86
- }
87
- },
88
- "download_size": 7705,
89
- "dataset_size": 9908,
90
- "size_in_bytes": 17613
91
- },
92
- "adv_mnli": {
93
- "description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n",
94
- "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n",
95
- "homepage": "https://adversarialglue.github.io/",
96
- "license": "",
97
- "features": {
98
- "premise": {
99
- "dtype": "string",
100
- "_type": "Value"
101
- },
102
- "hypothesis": {
103
- "dtype": "string",
104
- "_type": "Value"
105
- },
106
- "label": {
107
- "names": [
108
- "entailment",
109
- "neutral",
110
- "contradiction"
111
- ],
112
- "_type": "ClassLabel"
113
- },
114
- "idx": {
115
- "dtype": "int32",
116
- "_type": "Value"
117
- }
118
- },
119
- "builder_name": "parquet",
120
- "dataset_name": "adv_glue",
121
- "config_name": "adv_mnli",
122
- "version": {
123
- "version_str": "1.0.0",
124
- "major": 1,
125
- "minor": 0,
126
- "patch": 0
127
- },
128
- "splits": {
129
- "validation": {
130
- "name": "validation",
131
- "num_bytes": 23712,
132
- "num_examples": 121,
133
- "dataset_name": null
134
- }
135
- },
136
- "download_size": 13485,
137
- "dataset_size": 23712,
138
- "size_in_bytes": 37197
139
- },
140
- "adv_mnli_mismatched": {
141
- "description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n",
142
- "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n",
143
- "homepage": "https://adversarialglue.github.io/",
144
- "license": "",
145
- "features": {
146
- "premise": {
147
- "dtype": "string",
148
- "_type": "Value"
149
- },
150
- "hypothesis": {
151
- "dtype": "string",
152
- "_type": "Value"
153
- },
154
- "label": {
155
- "names": [
156
- "entailment",
157
- "neutral",
158
- "contradiction"
159
- ],
160
- "_type": "ClassLabel"
161
- },
162
- "idx": {
163
- "dtype": "int32",
164
- "_type": "Value"
165
- }
166
- },
167
- "builder_name": "parquet",
168
- "dataset_name": "adv_glue",
169
- "config_name": "adv_mnli_mismatched",
170
- "version": {
171
- "version_str": "1.0.0",
172
- "major": 1,
173
- "minor": 0,
174
- "patch": 0
175
- },
176
- "splits": {
177
- "validation": {
178
- "name": "validation",
179
- "num_bytes": 40953,
180
- "num_examples": 162,
181
- "dataset_name": null
182
- }
183
- },
184
- "download_size": 25166,
185
- "dataset_size": 40953,
186
- "size_in_bytes": 66119
187
- },
188
- "adv_qnli": {
189
- "description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n",
190
- "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n",
191
- "homepage": "https://adversarialglue.github.io/",
192
- "license": "",
193
- "features": {
194
- "question": {
195
- "dtype": "string",
196
- "_type": "Value"
197
- },
198
- "sentence": {
199
- "dtype": "string",
200
- "_type": "Value"
201
- },
202
- "label": {
203
- "names": [
204
- "entailment",
205
- "not_entailment"
206
- ],
207
- "_type": "ClassLabel"
208
- },
209
- "idx": {
210
- "dtype": "int32",
211
- "_type": "Value"
212
- }
213
- },
214
- "builder_name": "parquet",
215
- "dataset_name": "adv_glue",
216
- "config_name": "adv_qnli",
217
- "version": {
218
- "version_str": "1.0.0",
219
- "major": 1,
220
- "minor": 0,
221
- "patch": 0
222
- },
223
- "splits": {
224
- "validation": {
225
- "name": "validation",
226
- "num_bytes": 34850,
227
- "num_examples": 148,
228
- "dataset_name": null
229
- }
230
- },
231
- "download_size": 19111,
232
- "dataset_size": 34850,
233
- "size_in_bytes": 53961
234
- },
235
- "adv_rte": {
236
- "description": "Adversarial GLUE Benchmark (AdvGLUE) is a comprehensive robustness evaluation benchmark\nthat focuses on the adversarial robustness evaluation of language models. It covers five\nnatural language understanding tasks from the famous GLUE tasks and is an adversarial\nversion of GLUE benchmark.\n",
237
- "citation": "@article{Wang2021AdversarialGA,\n title={Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models},\n author={Boxin Wang and Chejian Xu and Shuohang Wang and Zhe Gan and Yu Cheng and Jianfeng Gao and Ahmed Hassan Awadallah and B. Li},\n journal={ArXiv},\n year={2021},\n volume={abs/2111.02840}\n}\n",
238
- "homepage": "https://adversarialglue.github.io/",
239
- "license": "",
240
- "features": {
241
- "sentence1": {
242
- "dtype": "string",
243
- "_type": "Value"
244
- },
245
- "sentence2": {
246
- "dtype": "string",
247
- "_type": "Value"
248
- },
249
- "label": {
250
- "names": [
251
- "entailment",
252
- "not_entailment"
253
- ],
254
- "_type": "ClassLabel"
255
- },
256
- "idx": {
257
- "dtype": "int32",
258
- "_type": "Value"
259
- }
260
- },
261
- "builder_name": "adv_glue",
262
- "dataset_name": "adv_glue",
263
- "config_name": "adv_rte",
264
- "version": {
265
- "version_str": "1.0.0",
266
- "description": "",
267
- "major": 1,
268
- "minor": 0,
269
- "patch": 0
270
- },
271
- "splits": {
272
- "validation": {
273
- "name": "validation",
274
- "num_bytes": 25979,
275
- "num_examples": 81,
276
- "dataset_name": null
277
- }
278
- },
279
- "download_size": 15872,
280
- "dataset_size": 25979,
281
- "size_in_bytes": 41851
282
- }
283
- }