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Datasets:
cardiffnlp
/
databench

Tasks:
Table Question Answering
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Spanish
Size:
1K - 10K
Tags:
table-question-answering
table
qa
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files Community
3
jorses commited on
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·
1 Parent(s): 76796bc

Update README.md

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  1. README.md +197 -65
README.md CHANGED
@@ -14,71 +14,203 @@ task_categories:
14
  configs:
15
  - config_name: qa
16
  data_files:
17
- - data/001_Forbes/qa.parquet
18
- - data/002_Titanic/qa.parquet
19
- - data/003_Love/qa.parquet
20
- - data/004_Taxi/qa.parquet
21
- - data/005_NYC/qa.parquet
22
- - data/006_London/qa.parquet
23
- - data/007_Fifa/qa.parquet
24
- - data/008_Tornados/qa.parquet
25
- - data/009_Central/qa.parquet
26
- - data/010_ECommerce/qa.parquet
27
- - data/011_SF/qa.parquet
28
- - data/012_Heart/qa.parquet
29
- - data/013_Roller/qa.parquet
30
- - data/014_Airbnb/qa.parquet
31
- - data/015_Food/qa.parquet
32
- - data/016_Holiday/qa.parquet
33
- - data/017_Hacker/qa.parquet
34
- - data/018_Staff/qa.parquet
35
- - data/019_Aircraft/qa.parquet
36
- - data/020_Real/qa.parquet
37
- - data/021_Telco/qa.parquet
38
- - data/022_Airbnbs/qa.parquet
39
- - data/023_Climate/qa.parquet
40
- - data/024_Salary/qa.parquet
41
- - data/025_Data/qa.parquet
42
- - data/026_Predicting/qa.parquet
43
- - data/027_Supermarket/qa.parquet
44
- - data/028_Predict/qa.parquet
45
- - data/029_NYTimes/qa.parquet
46
- - data/030_Professionals/qa.parquet
47
- - data/031_Trustpilot/qa.parquet
48
- - data/032_Delicatessen/qa.parquet
49
- - data/033_Employee/qa.parquet
50
- - data/034_World/qa.parquet
51
- - data/035_Billboard/qa.parquet
52
- - data/036_US/qa.parquet
53
- - data/037_Ted/qa.parquet
54
- - data/038_Stroke/qa.parquet
55
- - data/039_Happy/qa.parquet
56
- - data/040_Speed/qa.parquet
57
- - data/041_Airline/qa.parquet
58
- - data/042_Predict/qa.parquet
59
- - data/043_Predict/qa.parquet
60
- - data/044_IMDb/qa.parquet
61
- - data/045_Predict/qa.parquet
62
- - data/046_120/qa.parquet
63
- - data/047_Bank/qa.parquet
64
- - data/048_Data/qa.parquet
65
- - data/049_Boris/qa.parquet
66
- - data/050_ING/qa.parquet
67
- - data/051_Pokemon/qa.parquet
68
- - data/052_Professional/qa.parquet
69
- - data/053_Patents/qa.parquet
70
- - data/054_Joe/qa.parquet
71
- - data/055_German/qa.parquet
72
- - data/056_Emoji/qa.parquet
73
- - data/057_Spain/qa.parquet
74
- - data/058_US/qa.parquet
75
- - data/059_Second/qa.parquet
76
- - data/060_Bakery/qa.parquet
77
- - data/061_Disneyland/qa.parquet
78
- - data/062_Trump/qa.parquet
79
- - data/063_Influencers/qa.parquet
80
- - data/064_Clustering/qa.parquet
81
- - data/065_RFM/qa.parquet
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
82
  - config_name: 001_Forbes
83
  data_files:
84
  - split: full
 
14
  configs:
15
  - config_name: qa
16
  data_files:
17
+ - split: all
18
+ path:
19
+ - data/001_Forbes/qa.parquet
20
+ - data/002_Titanic/qa.parquet
21
+ - data/003_Love/qa.parquet
22
+ - data/004_Taxi/qa.parquet
23
+ - data/005_NYC/qa.parquet
24
+ - data/006_London/qa.parquet
25
+ - data/007_Fifa/qa.parquet
26
+ - data/008_Tornados/qa.parquet
27
+ - data/009_Central/qa.parquet
28
+ - data/010_ECommerce/qa.parquet
29
+ - data/011_SF/qa.parquet
30
+ - data/012_Heart/qa.parquet
31
+ - data/013_Roller/qa.parquet
32
+ - data/014_Airbnb/qa.parquet
33
+ - data/015_Food/qa.parquet
34
+ - data/016_Holiday/qa.parquet
35
+ - data/017_Hacker/qa.parquet
36
+ - data/018_Staff/qa.parquet
37
+ - data/019_Aircraft/qa.parquet
38
+ - data/020_Real/qa.parquet
39
+ - data/021_Telco/qa.parquet
40
+ - data/022_Airbnbs/qa.parquet
41
+ - data/023_Climate/qa.parquet
42
+ - data/024_Salary/qa.parquet
43
+ - data/025_Data/qa.parquet
44
+ - data/026_Predicting/qa.parquet
45
+ - data/027_Supermarket/qa.parquet
46
+ - data/028_Predict/qa.parquet
47
+ - data/029_NYTimes/qa.parquet
48
+ - data/030_Professionals/qa.parquet
49
+ - data/031_Trustpilot/qa.parquet
50
+ - data/032_Delicatessen/qa.parquet
51
+ - data/033_Employee/qa.parquet
52
+ - data/034_World/qa.parquet
53
+ - data/035_Billboard/qa.parquet
54
+ - data/036_US/qa.parquet
55
+ - data/037_Ted/qa.parquet
56
+ - data/038_Stroke/qa.parquet
57
+ - data/039_Happy/qa.parquet
58
+ - data/040_Speed/qa.parquet
59
+ - data/041_Airline/qa.parquet
60
+ - data/042_Predict/qa.parquet
61
+ - data/043_Predict/qa.parquet
62
+ - data/044_IMDb/qa.parquet
63
+ - data/045_Predict/qa.parquet
64
+ - data/046_120/qa.parquet
65
+ - data/047_Bank/qa.parquet
66
+ - data/048_Data/qa.parquet
67
+ - data/049_Boris/qa.parquet
68
+ - data/050_ING/qa.parquet
69
+ - data/051_Pokemon/qa.parquet
70
+ - data/052_Professional/qa.parquet
71
+ - data/053_Patents/qa.parquet
72
+ - data/054_Joe/qa.parquet
73
+ - data/055_German/qa.parquet
74
+ - data/056_Emoji/qa.parquet
75
+ - data/057_Spain/qa.parquet
76
+ - data/058_US/qa.parquet
77
+ - data/059_Second/qa.parquet
78
+ - data/060_Bakery/qa.parquet
79
+ - data/061_Disneyland/qa.parquet
80
+ - data/062_Trump/qa.parquet
81
+ - data/063_Influencers/qa.parquet
82
+ - data/064_Clustering/qa.parquet
83
+ - data/065_RFM/qa.parquet
84
+ - split: 001_Forbes
85
+ path: data/001_Forbes/qa.parquet
86
+ - split: 002_Titanic
87
+ path: data/002_Titanic/qa.parquet
88
+ - split: 003_Love
89
+ path: data/003_Love/qa.parquet
90
+ - split: 004_Taxi
91
+ path: data/004_Taxi/qa.parquet
92
+ - split: 005_NYC
93
+ path: data/005_NYC/qa.parquet
94
+ - split: 006_London
95
+ path: data/006_London/qa.parquet
96
+ - split: 007_Fifa
97
+ path: data/007_Fifa/qa.parquet
98
+ - split: 008_Tornados
99
+ path: data/008_Tornados/qa.parquet
100
+ - split: 009_Central
101
+ path: data/009_Central/qa.parquet
102
+ - split: 010_ECommerce
103
+ path: data/010_ECommerce/qa.parquet
104
+ - split: 011_SF
105
+ path: data/011_SF/qa.parquet
106
+ - split: 012_Heart
107
+ path: data/012_Heart/qa.parquet
108
+ - split: 013_Roller
109
+ path: data/013_Roller/qa.parquet
110
+ - split: 014_Airbnb
111
+ path: data/014_Airbnb/qa.parquet
112
+ - split: 015_Food
113
+ path: data/015_Food/qa.parquet
114
+ - split: 016_Holiday
115
+ path: data/016_Holiday/qa.parquet
116
+ - split: 017_Hacker
117
+ path: data/017_Hacker/qa.parquet
118
+ - split: 018_Staff
119
+ path: data/018_Staff/qa.parquet
120
+ - split: 019_Aircraft
121
+ path: data/019_Aircraft/qa.parquet
122
+ - split: 020_Real
123
+ path: data/020_Real/qa.parquet
124
+ - split: 021_Telco
125
+ path: data/021_Telco/qa.parquet
126
+ - split: 022_Airbnbs
127
+ path: data/022_Airbnbs/qa.parquet
128
+ - split: 023_Climate
129
+ path: data/023_Climate/qa.parquet
130
+ - split: 024_Salary
131
+ path: data/024_Salary/qa.parquet
132
+ - split: 025_Data
133
+ path: data/025_Data/qa.parquet
134
+ - split: 026_Predicting
135
+ path: data/026_Predicting/qa.parquet
136
+ - split: 027_Supermarket
137
+ path: data/027_Supermarket/qa.parquet
138
+ - split: 028_Predict
139
+ path: data/028_Predict/qa.parquet
140
+ - split: 029_NYTimes
141
+ path: data/029_NYTimes/qa.parquet
142
+ - split: 030_Professionals
143
+ path: data/030_Professionals/qa.parquet
144
+ - split: 031_Trustpilot
145
+ path: data/031_Trustpilot/qa.parquet
146
+ - split: 032_Delicatessen
147
+ path: data/032_Delicatessen/qa.parquet
148
+ - split: 033_Employee
149
+ path: data/033_Employee/qa.parquet
150
+ - split: 034_World
151
+ path: data/034_World/qa.parquet
152
+ - split: 035_Billboard
153
+ path: data/035_Billboard/qa.parquet
154
+ - split: 036_US
155
+ path: data/036_US/qa.parquet
156
+ - split: 037_Ted
157
+ path: data/037_Ted/qa.parquet
158
+ - split: 038_Stroke
159
+ path: data/038_Stroke/qa.parquet
160
+ - split: 039_Happy
161
+ path: data/039_Happy/qa.parquet
162
+ - split: 040_Speed
163
+ path: data/040_Speed/qa.parquet
164
+ - split: 041_Airline
165
+ path: data/041_Airline/qa.parquet
166
+ - split: 042_Predict
167
+ path: data/042_Predict/qa.parquet
168
+ - split: 043_Predict
169
+ path: data/043_Predict/qa.parquet
170
+ - split: 044_IMDb
171
+ path: data/044_IMDb/qa.parquet
172
+ - split: 045_Predict
173
+ path: data/045_Predict/qa.parquet
174
+ - split: "046_120"
175
+ path: data/046_120/qa.parquet
176
+ - split: 047_Bank
177
+ path: data/047_Bank/qa.parquet
178
+ - split: 048_Data
179
+ path: data/048_Data/qa.parquet
180
+ - split: 049_Boris
181
+ path: data/049_Boris/qa.parquet
182
+ - split: 050_ING
183
+ path: data/050_ING/qa.parquet
184
+ - split: 051_Pokemon
185
+ path: data/051_Pokemon/qa.parquet
186
+ - split: 052_Professional
187
+ path: data/052_Professional/qa.parquet
188
+ - split: 053_Patents
189
+ path: data/053_Patents/qa.parquet
190
+ - split: 054_Joe
191
+ path: data/054_Joe/qa.parquet
192
+ - split: 055_German
193
+ path: data/055_German/qa.parquet
194
+ - split: 056_Emoji
195
+ path: data/056_Emoji/qa.parquet
196
+ - split: 057_Spain
197
+ path: data/057_Spain/qa.parquet
198
+ - split: 058_US
199
+ path: data/058_US/qa.parquet
200
+ - split: 059_Second
201
+ path: data/059_Second/qa.parquet
202
+ - split: 060_Bakery
203
+ path: data/060_Bakery/qa.parquet
204
+ - split: 061_Disneyland
205
+ path: data/061_Disneyland/qa.parquet
206
+ - split: 062_Trump
207
+ path: data/062_Trump/qa.parquet
208
+ - split: 063_Influencers
209
+ path: data/063_Influencers/qa.parquet
210
+ - split: 064_Clustering
211
+ path: data/064_Clustering/qa.parquet
212
+ - split: 065_RFM
213
+ path: data/065_RFM/qa.parquet
214
  - config_name: 001_Forbes
215
  data_files:
216
  - split: full