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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 Viewer Files Community
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  1. README.md +330 -332
  2. data/001_Forbes/qa.parquet +0 -0
  3. data/002_Titanic/qa.parquet +0 -0
  4. data/002_Titanic/sample.parquet +0 -0
  5. data/003_Love/qa.parquet +0 -0
  6. data/003_Love/sample.parquet +0 -0
  7. data/004_Taxi/qa.parquet +0 -0
  8. data/004_Taxi/sample.parquet +0 -0
  9. data/005_NYC/qa.parquet +0 -0
  10. data/005_NYC/sample.parquet +0 -0
  11. data/006_London/qa.parquet +0 -0
  12. data/006_London/sample.parquet +0 -0
  13. data/007_Fifa/qa.parquet +0 -0
  14. data/007_Fifa/sample.parquet +0 -0
  15. data/008_Tornados/qa.parquet +0 -0
  16. data/008_Tornados/sample.parquet +0 -0
  17. data/009_Central/qa.parquet +0 -0
  18. data/009_Central/sample.parquet +0 -0
  19. data/010_ECommerce/qa.parquet +0 -0
  20. data/010_ECommerce/sample.parquet +0 -0
  21. data/011_SF/qa.parquet +0 -0
  22. data/011_SF/sample.parquet +0 -0
  23. data/012_Heart/qa.parquet +0 -0
  24. data/012_Heart/sample.parquet +0 -0
  25. data/013_Roller/qa.parquet +0 -0
  26. data/013_Roller/sample.parquet +0 -0
  27. data/014_Airbnb/qa.parquet +0 -0
  28. data/014_Airbnb/sample.parquet +0 -0
  29. data/015_Food/qa.parquet +0 -0
  30. data/015_Food/sample.parquet +0 -0
  31. data/016_Holiday/qa.parquet +0 -0
  32. data/016_Holiday/sample.parquet +0 -0
  33. data/017_Hacker/qa.parquet +0 -0
  34. data/017_Hacker/sample.parquet +0 -0
  35. data/018_Staff/qa.parquet +0 -0
  36. data/018_Staff/sample.parquet +0 -0
  37. data/019_Aircraft/qa.parquet +0 -0
  38. data/019_Aircraft/sample.parquet +0 -0
  39. data/020_Real/qa.parquet +0 -0
  40. data/020_Real/sample.parquet +0 -0
  41. data/021_Telco/qa.parquet +0 -0
  42. data/021_Telco/sample.parquet +0 -0
  43. data/022_Airbnbs/qa.parquet +0 -0
  44. data/022_Airbnbs/sample.parquet +0 -0
  45. data/023_Climate/qa.parquet +0 -0
  46. data/023_Climate/sample.parquet +0 -0
  47. data/024_Salary/qa.parquet +0 -0
  48. data/024_Salary/sample.parquet +0 -0
  49. data/025_Data/qa.parquet +0 -0
  50. data/025_Data/sample.parquet +0 -0
README.md CHANGED
@@ -14,414 +14,412 @@ task_categories:
14
  configs:
15
  - config_name: qa
16
  data_files:
17
- - data/001_Forbes/qa.csv
18
- - data/002_Titanic/qa.csv
19
- - data/003_Love/qa.csv
20
- - data/004_Taxi/qa.csv
21
- - data/005_NYC/qa.csv
22
- - data/006_London/qa.csv
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- - data/007_Fifa/qa.csv
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- - data/008_Tornados/qa.csv
25
- - data/009_Central/qa.csv
26
- - data/010_ECommerce/qa.csv
27
- - data/011_SF/qa.csv
28
- - data/012_Heart/qa.csv
29
- - data/013_Roller/qa.csv
30
- - data/014_Airbnb/qa.csv
31
- - data/015_Food/qa.csv
32
- - data/016_Holiday/qa.csv
33
- - data/017_Hacker/qa.csv
34
- - data/018_Staff/qa.csv
35
- - data/019_Aircraft/qa.csv
36
- - data/020_Real/qa.csv
37
- - data/021_Telco/qa.csv
38
- - data/022_Airbnbs/qa.csv
39
- - data/023_Climate/qa.csv
40
- - data/024_Salary/qa.csv
41
- - data/025_Data/qa.csv
42
- - data/026_Predicting/qa.csv
43
- - data/027_Supermarket/qa.csv
44
- - data/028_Predict/qa.csv
45
- - data/029_NYTimes/qa.csv
46
- - data/030_Professionals/qa.csv
47
- - data/031_Trustpilot/qa.csv
48
- - data/032_Delicatessen/qa.csv
49
- - data/033_Employee/qa.csv
50
- - data/034_World/qa.csv
51
- - data/035_Billboard/qa.csv
52
- - data/036_US/qa.csv
53
- - data/037_Ted/qa.csv
54
- - data/038_Stroke/qa.csv
55
- - data/039_Happy/qa.csv
56
- - data/040_Speed/qa.csv
57
- - data/041_Airline/qa.csv
58
- - data/042_Predict/qa.csv
59
- - data/043_Predict/qa.csv
60
- - data/044_IMDb/qa.csv
61
- - data/045_Predict/qa.csv
62
- - data/046_120/qa.csv
63
- - data/047_Bank/qa.csv
64
- - data/048_Data/qa.csv
65
- - data/049_Boris/qa.csv
66
- - data/050_ING/qa.csv
67
- - data/051_Pokemon/qa.csv
68
- - data/052_Professional/qa.csv
69
- - data/053_Patents/qa.csv
70
- - data/054_Joe/qa.csv
71
- - data/055_German/qa.csv
72
- - data/056_Emoji/qa.csv
73
- - data/057_Spain/qa.csv
74
- - data/058_US/qa.csv
75
- - data/059_Second/qa.csv
76
- - data/060_Bakery/qa.csv
77
- - data/061_Disneyland/qa.csv
78
- - data/062_Trump/qa.csv
79
- - data/063_Influencers/qa.csv
80
- - data/064_Clustering/qa.csv
81
- - data/065_RFM/qa.csv
82
  - config_name: 001_Forbes
83
  data_files:
84
  - split: full
85
  path: data/001_Forbes/all.parquet
86
- format: parquet
87
  - split: lite
88
  path: data/001_Forbes/sample.parquet
89
- format: parquet
90
- # - config_name: data
91
- # data_files:
92
- # - split: 001_Forbes
93
- # path: data/001_Forbes/all.parquet
94
- # - split: 002_Titanic
95
- # path: data/002_Titanic/all.parquet
96
- # - split: 003_Love
97
- # path: data/003_Love/all.parquet
98
- # - split: 004_Taxi
99
- # path: data/004_Taxi/all.parquet
100
- # - split: 005_NYC
101
- # path: data/005_NYC/all.parquet
102
- # - split: 006_London
103
- # path: data/006_London/all.parquet
104
- # - split: 007_Fifa
105
- # path: data/007_Fifa/all.parquet
106
- # - split: 008_Tornados
107
- # path: data/008_Tornados/all.parquet
108
- # - split: 009_Central
109
- # path: data/009_Central/all.parquet
110
- # - split: 010_ECommerce
111
- # path: data/010_ECommerce/all.parquet
112
- # - split: 011_SF
113
- # path: data/011_SF/all.parquet
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- # - split: 012_Heart
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- # path: data/012_Heart/all.parquet
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- # - split: 013_Roller
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- # path: data/013_Roller/all.parquet
118
- # - split: 014_Airbnb
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- # path: data/014_Airbnb/all.parquet
120
- # - split: 015_Food
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- # path: data/015_Food/all.parquet
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- # - split: 016_Holiday
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- # path: data/016_Holiday/all.parquet
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- # - split: 017_Hacker
125
- # path: data/017_Hacker/all.parquet
126
- # - split: 018_Staff
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- # path: data/018_Staff/all.parquet
128
- # - split: 019_Aircraft
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- # path: data/019_Aircraft/all.parquet
130
- # - split: 020_Real
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- # path: data/020_Real/all.parquet
132
- # - split: 021_Telco
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- # path: data/021_Telco/all.parquet
134
- # - split: 022_Airbnbs
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- # path: data/022_Airbnbs/all.parquet
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- # - split: 023_Climate
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- # path: data/023_Climate/all.parquet
138
- # - split: 024_Salary
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- # path: data/024_Salary/all.parquet
140
- # - split: 025_Data
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- # path: data/025_Data/all.parquet
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- # - split: 026_Predicting
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- # path: data/026_Predicting/all.parquet
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- # - split: 027_Supermarket
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- # path: data/027_Supermarket/all.parquet
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- # - split: 028_Predict
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- # path: data/028_Predict/all.parquet
148
- # - split: 029_NYTimes
149
- # path: data/029_NYTimes/all.parquet
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- # - split: 030_Professionals
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- # path: data/030_Professionals/all.parquet
152
- # - split: 031_Trustpilot
153
- # path: data/031_Trustpilot/all.parquet
154
- # - split: 032_Delicatessen
155
- # path: data/032_Delicatessen/all.parquet
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- # - split: 033_Employee
157
- # path: data/033_Employee/all.parquet
158
- # - split: 034_World
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- # path: data/034_World/all.parquet
160
- # - split: 035_Billboard
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- # path: data/035_Billboard/all.parquet
162
- # - split: 036_US
163
- # path: data/036_US/all.parquet
164
- # - split: 037_Ted
165
- # path: data/037_Ted/all.parquet
166
- # - split: 038_Stroke
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- # path: data/038_Stroke/all.parquet
168
- # - split: 039_Happy
169
- # path: data/039_Happy/all.parquet
170
- # - split: 040_Speed
171
- # path: data/040_Speed/all.parquet
172
- # - split: 041_Airline
173
- # path: data/041_Airline/all.parquet
174
- # - split: 042_Predict
175
- # path: data/042_Predict/all.parquet
176
- # - split: 043_Predict
177
- # path: data/043_Predict/all.parquet
178
- # - split: 044_IMDb
179
- # path: data/044_IMDb/all.parquet
180
- # - split: 045_Predict
181
- # path: data/045_Predict/all.parquet
182
- # - split: "046_120"
183
- # path: data/046_120/all.parquet
184
- # - split: 047_Bank
185
- # path: data/047_Bank/all.parquet
186
- # - split: 048_Data
187
- # path: data/048_Data/all.parquet
188
- # - split: 049_Boris
189
- # path: data/049_Boris/all.parquet
190
- # - split: 050_ING
191
- # path: data/050_ING/all.parquet
192
- # - split: 051_Pokemon
193
- # path: data/051_Pokemon/all.parquet
194
- # - split: 052_Professional
195
- # path: data/052_Professional/all.parquet
196
- # - split: 053_Patents
197
- # path: data/053_Patents/all.parquet
198
- # - split: 054_Joe
199
- # path: data/054_Joe/all.parquet
200
- # - split: 055_German
201
- # path: data/055_German/all.parquet
202
- # - split: 056_Emoji
203
- # path: data/056_Emoji/all.parquet
204
- # - split: 057_Spain
205
- # path: data/057_Spain/all.parquet
206
- # - split: 058_US
207
- # path: data/058_US/all.parquet
208
- # - split: 059_Second
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- # path: data/059_Second/all.parquet
210
- # - split: 060_Bakery
211
- # path: data/060_Bakery/all.parquet
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- # - split: 061_Disneyland
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- # path: data/061_Disneyland/all.parquet
214
- # - split: 062_Trump
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- # path: data/062_Trump/all.parquet
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- # - split: 063_Influencers
217
- # path: data/063_Influencers/all.parquet
218
- # - split: 064_Clustering
219
- # path: data/064_Clustering/all.parquet
220
- # - split: 065_RFM
221
- # path: data/065_RFM/all.parquet
222
  - config_name: data_lite
223
  data_files:
224
  - split: 001_Forbes
225
- path: data/001_Forbes/sample.csv
226
  - split: 002_Titanic
227
- path: data/002_Titanic/sample.csv
228
  - split: 003_Love
229
- path: data/003_Love/sample.csv
230
  - split: 004_Taxi
231
- path: data/004_Taxi/sample.csv
232
  - split: 005_NYC
233
- path: data/005_NYC/sample.csv
234
  - split: 006_London
235
- path: data/006_London/sample.csv
236
  - split: 007_Fifa
237
- path: data/007_Fifa/sample.csv
238
  - split: 008_Tornados
239
- path: data/008_Tornados/sample.csv
240
  - split: 009_Central
241
- path: data/009_Central/sample.csv
242
  - split: 010_ECommerce
243
- path: data/010_ECommerce/sample.csv
244
  - split: 011_SF
245
- path: data/011_SF/sample.csv
246
  - split: 012_Heart
247
- path: data/012_Heart/sample.csv
248
  - split: 013_Roller
249
- path: data/013_Roller/sample.csv
250
  - split: 014_Airbnb
251
- path: data/014_Airbnb/sample.csv
252
  - split: 015_Food
253
- path: data/015_Food/sample.csv
254
  - split: 016_Holiday
255
- path: data/016_Holiday/sample.csv
256
  - split: 017_Hacker
257
- path: data/017_Hacker/sample.csv
258
  - split: 018_Staff
259
- path: data/018_Staff/sample.csv
260
  - split: 019_Aircraft
261
- path: data/019_Aircraft/sample.csv
262
  - split: 020_Real
263
- path: data/020_Real/sample.csv
264
  - split: 021_Telco
265
- path: data/021_Telco/sample.csv
266
  - split: 022_Airbnbs
267
- path: data/022_Airbnbs/sample.csv
268
  - split: 023_Climate
269
- path: data/023_Climate/sample.csv
270
  - split: 024_Salary
271
- path: data/024_Salary/sample.csv
272
  - split: 025_Data
273
- path: data/025_Data/sample.csv
274
  - split: 026_Predicting
275
- path: data/026_Predicting/sample.csv
276
  - split: 027_Supermarket
277
- path: data/027_Supermarket/sample.csv
278
  - split: 028_Predict
279
- path: data/028_Predict/sample.csv
280
  - split: 029_NYTimes
281
- path: data/029_NYTimes/sample.csv
282
  - split: 030_Professionals
283
- path: data/030_Professionals/sample.csv
284
  - split: 031_Trustpilot
285
- path: data/031_Trustpilot/sample.csv
286
  - split: 032_Delicatessen
287
- path: data/032_Delicatessen/sample.csv
288
  - split: 033_Employee
289
- path: data/033_Employee/sample.csv
290
  - split: 034_World
291
- path: data/034_World/sample.csv
292
  - split: 035_Billboard
293
- path: data/035_Billboard/sample.csv
294
  - split: 036_US
295
- path: data/036_US/sample.csv
296
  - split: 037_Ted
297
- path: data/037_Ted/sample.csv
298
  - split: 038_Stroke
299
- path: data/038_Stroke/sample.csv
300
  - split: 039_Happy
301
- path: data/039_Happy/sample.csv
302
  - split: 040_Speed
303
- path: data/040_Speed/sample.csv
304
  - split: 041_Airline
305
- path: data/041_Airline/sample.csv
306
  - split: 042_Predict
307
- path: data/042_Predict/sample.csv
308
  - split: 043_Predict
309
- path: data/043_Predict/sample.csv
310
  - split: 044_IMDb
311
- path: data/044_IMDb/sample.csv
312
  - split: 045_Predict
313
- path: data/045_Predict/sample.csv
314
  - split: "046_120"
315
- path: data/046_120/sample.csv
316
  - split: 047_Bank
317
- path: data/047_Bank/sample.csv
318
  - split: 048_Data
319
- path: data/048_Data/sample.csv
320
  - split: 049_Boris
321
- path: data/049_Boris/sample.csv
322
  - split: 050_ING
323
- path: data/050_ING/sample.csv
324
  - split: 051_Pokemon
325
- path: data/051_Pokemon/sample.csv
326
  - split: 052_Professional
327
- path: data/052_Professional/sample.csv
328
  - split: 053_Patents
329
- path: data/053_Patents/sample.csv
330
  - split: 054_Joe
331
- path: data/054_Joe/sample.csv
332
  - split: 055_German
333
- path: data/055_German/sample.csv
334
  - split: 056_Emoji
335
- path: data/056_Emoji/sample.csv
336
  - split: 057_Spain
337
- path: data/057_Spain/sample.csv
338
  - split: 058_US
339
- path: data/058_US/sample.csv
340
  - split: 059_Second
341
- path: data/059_Second/sample.csv
342
  - split: 060_Bakery
343
- path: data/060_Bakery/sample.csv
344
  - split: 061_Disneyland
345
- path: data/061_Disneyland/sample.csv
346
  - split: 062_Trump
347
- path: data/062_Trump/sample.csv
348
  - split: 063_Influencers
349
- path: data/063_Influencers/sample.csv
350
  - split: 064_Clustering
351
- path: data/064_Clustering/sample.csv
352
  - split: 065_RFM
353
- path: data/065_RFM/sample.csv
354
  - config_name: semeval
355
  data_files:
356
  - split: train
357
  path:
358
- - data/001_Forbes/qa.csv
359
- - data/002_Titanic/qa.csv
360
- - data/003_Love/qa.csv
361
- - data/004_Taxi/qa.csv
362
- - data/005_NYC/qa.csv
363
- - data/006_London/qa.csv
364
- - data/007_Fifa/qa.csv
365
- - data/008_Tornados/qa.csv
366
- - data/009_Central/qa.csv
367
- - data/010_ECommerce/qa.csv
368
- - data/011_SF/qa.csv
369
- - data/012_Heart/qa.csv
370
- - data/013_Roller/qa.csv
371
- - data/014_Airbnb/qa.csv
372
- - data/015_Food/qa.csv
373
- - data/016_Holiday/qa.csv
374
- - data/017_Hacker/qa.csv
375
- - data/018_Staff/qa.csv
376
- - data/019_Aircraft/qa.csv
377
- - data/020_Real/qa.csv
378
- - data/021_Telco/qa.csv
379
- - data/022_Airbnbs/qa.csv
380
- - data/023_Climate/qa.csv
381
- - data/024_Salary/qa.csv
382
- - data/025_Data/qa.csv
383
- - data/026_Predicting/qa.csv
384
- - data/027_Supermarket/qa.csv
385
- - data/028_Predict/qa.csv
386
- - data/029_NYTimes/qa.csv
387
- - data/030_Professionals/qa.csv
388
- - data/031_Trustpilot/qa.csv
389
- - data/032_Delicatessen/qa.csv
390
- - data/033_Employee/qa.csv
391
- - data/034_World/qa.csv
392
- - data/035_Billboard/qa.csv
393
- - data/036_US/qa.csv
394
- - data/037_Ted/qa.csv
395
- - data/038_Stroke/qa.csv
396
- - data/039_Happy/qa.csv
397
- - data/040_Speed/qa.csv
398
- - data/041_Airline/qa.csv
399
- - data/042_Predict/qa.csv
400
- - data/043_Predict/qa.csv
401
- - data/044_IMDb/qa.csv
402
- - data/045_Predict/qa.csv
403
- - data/046_120/qa.csv
404
- - data/047_Bank/qa.csv
405
- - data/048_Data/qa.csv
406
- - data/049_Boris/qa.csv
407
  - split: test
408
  path:
409
- - data/050_ING/qa.csv
410
- - data/051_Pokemon/qa.csv
411
- - data/052_Professional/qa.csv
412
- - data/053_Patents/qa.csv
413
- - data/054_Joe/qa.csv
414
- - data/055_German/qa.csv
415
- - data/056_Emoji/qa.csv
416
- - data/057_Spain/qa.csv
417
- - data/058_US/qa.csv
418
- - data/059_Second/qa.csv
419
- - data/060_Bakery/qa.csv
420
- - data/061_Disneyland/qa.csv
421
- - data/062_Trump/qa.csv
422
- - data/063_Influencers/qa.csv
423
- - data/064_Clustering/qa.csv
424
- - data/065_RFM/qa.csv
425
 
426
  ---
427
  # 💾🏋️💾 DataBench 💾🏋️💾
@@ -453,7 +451,7 @@ By clicking on each name in the table below, you will be able to explore each da
453
  | 11 | [SF Police](https://public.graphext.com/ab815ab14f88115c/index.html) | 713107 | 35 | Social Networks and Surveys | [US Gov](https://catalog.data.gov/dataset/police-department-incident-reports-2018-to-present) |
454
  | 12 | [Heart Failure](https://public.graphext.com/245cec64075f5542/index.html) | 918 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) |
455
  | 13 | [Roller Coasters](https://public.graphext.com/1e550e6c24fc1930/index.html) | 1087 | 56 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/robikscube/rollercoaster-database) |
456
- | 14 | [Madrid Airbnbs](https://public.graphext.com/77265ea3a63e650f/index.html) | 20776 | 75 | Travel and Locations | [Inside Airbnb](http://data.insideairbnb.com/spain/comunidad-de-madrid/madrid/2023-09-07/data/listings.csv.gz) |
457
  | 15 | [Food Names](https://public.graphext.com/5aad4c5d6ef140b3/index.html) | 906 | 4 | Business | [Data World](https://data.world/alexandra/generic-food-database) |
458
  | 16 | [Holiday Package Sales](https://public.graphext.com/fbc34d3f24282e46/index.html) | 4888 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/susant4learning/holiday-package-purchase-prediction) |
459
  | 17 | [Hacker News](https://public.graphext.com/f20501a9d616b5a5/index.html) | 9429 | 20 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/hacker-news/hacker-news) |
@@ -510,8 +508,8 @@ By clicking on each name in the table below, you will be able to explore each da
510
  Each folder represents one dataset. You will find the following files within:
511
 
512
  * all.parquet: the processed data, with each column tagged with our typing system, in [parquet](https://arrow.apache.org/docs/python/parquet.html).
513
- * qa.csv: contains the human-made set of questions, tagged by type and columns used, for the dataset (sample_answer indicates the answers for DataBench lite)
514
- * sample.csv: sample containing 20 rows of the original dataset (DataBench lite)
515
  * info.yml: additional information about the dataset
516
 
517
  ## 🗂️ Column typing system
 
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
85
  path: data/001_Forbes/all.parquet
 
86
  - split: lite
87
  path: data/001_Forbes/sample.parquet
88
+ - config_name: data
89
+ data_files:
90
+ - split: 001_Forbes
91
+ path: data/001_Forbes/all.parquet
92
+ - split: 002_Titanic
93
+ path: data/002_Titanic/all.parquet
94
+ - split: 003_Love
95
+ path: data/003_Love/all.parquet
96
+ - split: 004_Taxi
97
+ path: data/004_Taxi/all.parquet
98
+ - split: 005_NYC
99
+ path: data/005_NYC/all.parquet
100
+ - split: 006_London
101
+ path: data/006_London/all.parquet
102
+ - split: 007_Fifa
103
+ path: data/007_Fifa/all.parquet
104
+ - split: 008_Tornados
105
+ path: data/008_Tornados/all.parquet
106
+ - split: 009_Central
107
+ path: data/009_Central/all.parquet
108
+ - split: 010_ECommerce
109
+ path: data/010_ECommerce/all.parquet
110
+ - split: 011_SF
111
+ path: data/011_SF/all.parquet
112
+ - split: 012_Heart
113
+ path: data/012_Heart/all.parquet
114
+ - split: 013_Roller
115
+ path: data/013_Roller/all.parquet
116
+ - split: 014_Airbnb
117
+ path: data/014_Airbnb/all.parquet
118
+ - split: 015_Food
119
+ path: data/015_Food/all.parquet
120
+ - split: 016_Holiday
121
+ path: data/016_Holiday/all.parquet
122
+ - split: 017_Hacker
123
+ path: data/017_Hacker/all.parquet
124
+ - split: 018_Staff
125
+ path: data/018_Staff/all.parquet
126
+ - split: 019_Aircraft
127
+ path: data/019_Aircraft/all.parquet
128
+ - split: 020_Real
129
+ path: data/020_Real/all.parquet
130
+ - split: 021_Telco
131
+ path: data/021_Telco/all.parquet
132
+ - split: 022_Airbnbs
133
+ path: data/022_Airbnbs/all.parquet
134
+ - split: 023_Climate
135
+ path: data/023_Climate/all.parquet
136
+ - split: 024_Salary
137
+ path: data/024_Salary/all.parquet
138
+ - split: 025_Data
139
+ path: data/025_Data/all.parquet
140
+ - split: 026_Predicting
141
+ path: data/026_Predicting/all.parquet
142
+ - split: 027_Supermarket
143
+ path: data/027_Supermarket/all.parquet
144
+ - split: 028_Predict
145
+ path: data/028_Predict/all.parquet
146
+ - split: 029_NYTimes
147
+ path: data/029_NYTimes/all.parquet
148
+ - split: 030_Professionals
149
+ path: data/030_Professionals/all.parquet
150
+ - split: 031_Trustpilot
151
+ path: data/031_Trustpilot/all.parquet
152
+ - split: 032_Delicatessen
153
+ path: data/032_Delicatessen/all.parquet
154
+ - split: 033_Employee
155
+ path: data/033_Employee/all.parquet
156
+ - split: 034_World
157
+ path: data/034_World/all.parquet
158
+ - split: 035_Billboard
159
+ path: data/035_Billboard/all.parquet
160
+ - split: 036_US
161
+ path: data/036_US/all.parquet
162
+ - split: 037_Ted
163
+ path: data/037_Ted/all.parquet
164
+ - split: 038_Stroke
165
+ path: data/038_Stroke/all.parquet
166
+ - split: 039_Happy
167
+ path: data/039_Happy/all.parquet
168
+ - split: 040_Speed
169
+ path: data/040_Speed/all.parquet
170
+ - split: 041_Airline
171
+ path: data/041_Airline/all.parquet
172
+ - split: 042_Predict
173
+ path: data/042_Predict/all.parquet
174
+ - split: 043_Predict
175
+ path: data/043_Predict/all.parquet
176
+ - split: 044_IMDb
177
+ path: data/044_IMDb/all.parquet
178
+ - split: 045_Predict
179
+ path: data/045_Predict/all.parquet
180
+ - split: "046_120"
181
+ path: data/046_120/all.parquet
182
+ - split: 047_Bank
183
+ path: data/047_Bank/all.parquet
184
+ - split: 048_Data
185
+ path: data/048_Data/all.parquet
186
+ - split: 049_Boris
187
+ path: data/049_Boris/all.parquet
188
+ - split: 050_ING
189
+ path: data/050_ING/all.parquet
190
+ - split: 051_Pokemon
191
+ path: data/051_Pokemon/all.parquet
192
+ - split: 052_Professional
193
+ path: data/052_Professional/all.parquet
194
+ - split: 053_Patents
195
+ path: data/053_Patents/all.parquet
196
+ - split: 054_Joe
197
+ path: data/054_Joe/all.parquet
198
+ - split: 055_German
199
+ path: data/055_German/all.parquet
200
+ - split: 056_Emoji
201
+ path: data/056_Emoji/all.parquet
202
+ - split: 057_Spain
203
+ path: data/057_Spain/all.parquet
204
+ - split: 058_US
205
+ path: data/058_US/all.parquet
206
+ - split: 059_Second
207
+ path: data/059_Second/all.parquet
208
+ - split: 060_Bakery
209
+ path: data/060_Bakery/all.parquet
210
+ - split: 061_Disneyland
211
+ path: data/061_Disneyland/all.parquet
212
+ - split: 062_Trump
213
+ path: data/062_Trump/all.parquet
214
+ - split: 063_Influencers
215
+ path: data/063_Influencers/all.parquet
216
+ - split: 064_Clustering
217
+ path: data/064_Clustering/all.parquet
218
+ - split: 065_RFM
219
+ path: data/065_RFM/all.parquet
 
220
  - config_name: data_lite
221
  data_files:
222
  - split: 001_Forbes
223
+ path: data/001_Forbes/sample.parquet
224
  - split: 002_Titanic
225
+ path: data/002_Titanic/sample.parquet
226
  - split: 003_Love
227
+ path: data/003_Love/sample.parquet
228
  - split: 004_Taxi
229
+ path: data/004_Taxi/sample.parquet
230
  - split: 005_NYC
231
+ path: data/005_NYC/sample.parquet
232
  - split: 006_London
233
+ path: data/006_London/sample.parquet
234
  - split: 007_Fifa
235
+ path: data/007_Fifa/sample.parquet
236
  - split: 008_Tornados
237
+ path: data/008_Tornados/sample.parquet
238
  - split: 009_Central
239
+ path: data/009_Central/sample.parquet
240
  - split: 010_ECommerce
241
+ path: data/010_ECommerce/sample.parquet
242
  - split: 011_SF
243
+ path: data/011_SF/sample.parquet
244
  - split: 012_Heart
245
+ path: data/012_Heart/sample.parquet
246
  - split: 013_Roller
247
+ path: data/013_Roller/sample.parquet
248
  - split: 014_Airbnb
249
+ path: data/014_Airbnb/sample.parquet
250
  - split: 015_Food
251
+ path: data/015_Food/sample.parquet
252
  - split: 016_Holiday
253
+ path: data/016_Holiday/sample.parquet
254
  - split: 017_Hacker
255
+ path: data/017_Hacker/sample.parquet
256
  - split: 018_Staff
257
+ path: data/018_Staff/sample.parquet
258
  - split: 019_Aircraft
259
+ path: data/019_Aircraft/sample.parquet
260
  - split: 020_Real
261
+ path: data/020_Real/sample.parquet
262
  - split: 021_Telco
263
+ path: data/021_Telco/sample.parquet
264
  - split: 022_Airbnbs
265
+ path: data/022_Airbnbs/sample.parquet
266
  - split: 023_Climate
267
+ path: data/023_Climate/sample.parquet
268
  - split: 024_Salary
269
+ path: data/024_Salary/sample.parquet
270
  - split: 025_Data
271
+ path: data/025_Data/sample.parquet
272
  - split: 026_Predicting
273
+ path: data/026_Predicting/sample.parquet
274
  - split: 027_Supermarket
275
+ path: data/027_Supermarket/sample.parquet
276
  - split: 028_Predict
277
+ path: data/028_Predict/sample.parquet
278
  - split: 029_NYTimes
279
+ path: data/029_NYTimes/sample.parquet
280
  - split: 030_Professionals
281
+ path: data/030_Professionals/sample.parquet
282
  - split: 031_Trustpilot
283
+ path: data/031_Trustpilot/sample.parquet
284
  - split: 032_Delicatessen
285
+ path: data/032_Delicatessen/sample.parquet
286
  - split: 033_Employee
287
+ path: data/033_Employee/sample.parquet
288
  - split: 034_World
289
+ path: data/034_World/sample.parquet
290
  - split: 035_Billboard
291
+ path: data/035_Billboard/sample.parquet
292
  - split: 036_US
293
+ path: data/036_US/sample.parquet
294
  - split: 037_Ted
295
+ path: data/037_Ted/sample.parquet
296
  - split: 038_Stroke
297
+ path: data/038_Stroke/sample.parquet
298
  - split: 039_Happy
299
+ path: data/039_Happy/sample.parquet
300
  - split: 040_Speed
301
+ path: data/040_Speed/sample.parquet
302
  - split: 041_Airline
303
+ path: data/041_Airline/sample.parquet
304
  - split: 042_Predict
305
+ path: data/042_Predict/sample.parquet
306
  - split: 043_Predict
307
+ path: data/043_Predict/sample.parquet
308
  - split: 044_IMDb
309
+ path: data/044_IMDb/sample.parquet
310
  - split: 045_Predict
311
+ path: data/045_Predict/sample.parquet
312
  - split: "046_120"
313
+ path: data/046_120/sample.parquet
314
  - split: 047_Bank
315
+ path: data/047_Bank/sample.parquet
316
  - split: 048_Data
317
+ path: data/048_Data/sample.parquet
318
  - split: 049_Boris
319
+ path: data/049_Boris/sample.parquet
320
  - split: 050_ING
321
+ path: data/050_ING/sample.parquet
322
  - split: 051_Pokemon
323
+ path: data/051_Pokemon/sample.parquet
324
  - split: 052_Professional
325
+ path: data/052_Professional/sample.parquet
326
  - split: 053_Patents
327
+ path: data/053_Patents/sample.parquet
328
  - split: 054_Joe
329
+ path: data/054_Joe/sample.parquet
330
  - split: 055_German
331
+ path: data/055_German/sample.parquet
332
  - split: 056_Emoji
333
+ path: data/056_Emoji/sample.parquet
334
  - split: 057_Spain
335
+ path: data/057_Spain/sample.parquet
336
  - split: 058_US
337
+ path: data/058_US/sample.parquet
338
  - split: 059_Second
339
+ path: data/059_Second/sample.parquet
340
  - split: 060_Bakery
341
+ path: data/060_Bakery/sample.parquet
342
  - split: 061_Disneyland
343
+ path: data/061_Disneyland/sample.parquet
344
  - split: 062_Trump
345
+ path: data/062_Trump/sample.parquet
346
  - split: 063_Influencers
347
+ path: data/063_Influencers/sample.parquet
348
  - split: 064_Clustering
349
+ path: data/064_Clustering/sample.parquet
350
  - split: 065_RFM
351
+ path: data/065_RFM/sample.parquet
352
  - config_name: semeval
353
  data_files:
354
  - split: train
355
  path:
356
+ - data/001_Forbes/qa.parquet
357
+ - data/002_Titanic/qa.parquet
358
+ - data/003_Love/qa.parquet
359
+ - data/004_Taxi/qa.parquet
360
+ - data/005_NYC/qa.parquet
361
+ - data/006_London/qa.parquet
362
+ - data/007_Fifa/qa.parquet
363
+ - data/008_Tornados/qa.parquet
364
+ - data/009_Central/qa.parquet
365
+ - data/010_ECommerce/qa.parquet
366
+ - data/011_SF/qa.parquet
367
+ - data/012_Heart/qa.parquet
368
+ - data/013_Roller/qa.parquet
369
+ - data/014_Airbnb/qa.parquet
370
+ - data/015_Food/qa.parquet
371
+ - data/016_Holiday/qa.parquet
372
+ - data/017_Hacker/qa.parquet
373
+ - data/018_Staff/qa.parquet
374
+ - data/019_Aircraft/qa.parquet
375
+ - data/020_Real/qa.parquet
376
+ - data/021_Telco/qa.parquet
377
+ - data/022_Airbnbs/qa.parquet
378
+ - data/023_Climate/qa.parquet
379
+ - data/024_Salary/qa.parquet
380
+ - data/025_Data/qa.parquet
381
+ - data/026_Predicting/qa.parquet
382
+ - data/027_Supermarket/qa.parquet
383
+ - data/028_Predict/qa.parquet
384
+ - data/029_NYTimes/qa.parquet
385
+ - data/030_Professionals/qa.parquet
386
+ - data/031_Trustpilot/qa.parquet
387
+ - data/032_Delicatessen/qa.parquet
388
+ - data/033_Employee/qa.parquet
389
+ - data/034_World/qa.parquet
390
+ - data/035_Billboard/qa.parquet
391
+ - data/036_US/qa.parquet
392
+ - data/037_Ted/qa.parquet
393
+ - data/038_Stroke/qa.parquet
394
+ - data/039_Happy/qa.parquet
395
+ - data/040_Speed/qa.parquet
396
+ - data/041_Airline/qa.parquet
397
+ - data/042_Predict/qa.parquet
398
+ - data/043_Predict/qa.parquet
399
+ - data/044_IMDb/qa.parquet
400
+ - data/045_Predict/qa.parquet
401
+ - data/046_120/qa.parquet
402
+ - data/047_Bank/qa.parquet
403
+ - data/048_Data/qa.parquet
404
+ - data/049_Boris/qa.parquet
405
  - split: test
406
  path:
407
+ - data/050_ING/qa.parquet
408
+ - data/051_Pokemon/qa.parquet
409
+ - data/052_Professional/qa.parquet
410
+ - data/053_Patents/qa.parquet
411
+ - data/054_Joe/qa.parquet
412
+ - data/055_German/qa.parquet
413
+ - data/056_Emoji/qa.parquet
414
+ - data/057_Spain/qa.parquet
415
+ - data/058_US/qa.parquet
416
+ - data/059_Second/qa.parquet
417
+ - data/060_Bakery/qa.parquet
418
+ - data/061_Disneyland/qa.parquet
419
+ - data/062_Trump/qa.parquet
420
+ - data/063_Influencers/qa.parquet
421
+ - data/064_Clustering/qa.parquet
422
+ - data/065_RFM/qa.parquet
423
 
424
  ---
425
  # 💾🏋️💾 DataBench 💾🏋️💾
 
451
  | 11 | [SF Police](https://public.graphext.com/ab815ab14f88115c/index.html) | 713107 | 35 | Social Networks and Surveys | [US Gov](https://catalog.data.gov/dataset/police-department-incident-reports-2018-to-present) |
452
  | 12 | [Heart Failure](https://public.graphext.com/245cec64075f5542/index.html) | 918 | 12 | Health | [Kaggle](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) |
453
  | 13 | [Roller Coasters](https://public.graphext.com/1e550e6c24fc1930/index.html) | 1087 | 56 | Sports and Entertainment | [Kaggle](https://www.kaggle.com/datasets/robikscube/rollercoaster-database) |
454
+ | 14 | [Madrid Airbnbs](https://public.graphext.com/77265ea3a63e650f/index.html) | 20776 | 75 | Travel and Locations | [Inside Airbnb](http://data.insideairbnb.com/spain/comunidad-de-madrid/madrid/2023-09-07/data/listings.parquet.gz) |
455
  | 15 | [Food Names](https://public.graphext.com/5aad4c5d6ef140b3/index.html) | 906 | 4 | Business | [Data World](https://data.world/alexandra/generic-food-database) |
456
  | 16 | [Holiday Package Sales](https://public.graphext.com/fbc34d3f24282e46/index.html) | 4888 | 20 | Travel and Locations | [Kaggle](https://www.kaggle.com/datasets/susant4learning/holiday-package-purchase-prediction) |
457
  | 17 | [Hacker News](https://public.graphext.com/f20501a9d616b5a5/index.html) | 9429 | 20 | Social Networks and Surveys | [Kaggle](https://www.kaggle.com/datasets/hacker-news/hacker-news) |
 
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  Each folder represents one dataset. You will find the following files within:
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  * all.parquet: the processed data, with each column tagged with our typing system, in [parquet](https://arrow.apache.org/docs/python/parquet.html).
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+ * qa.parquet: contains the human-made set of questions, tagged by type and columns used, for the dataset (sample_answer indicates the answers for DataBench lite)
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+ * sample.parquet: sample containing 20 rows of the original dataset (DataBench lite)
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  * info.yml: additional information about the dataset
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  ## 🗂️ Column typing system
data/001_Forbes/qa.parquet ADDED
Binary file (5.18 kB). View file
 
data/002_Titanic/qa.parquet ADDED
Binary file (4.92 kB). View file
 
data/002_Titanic/sample.parquet ADDED
Binary file (4 kB). View file
 
data/003_Love/qa.parquet ADDED
Binary file (5.61 kB). View file
 
data/003_Love/sample.parquet ADDED
Binary file (20.9 kB). View file
 
data/004_Taxi/qa.parquet ADDED
Binary file (4.92 kB). View file
 
data/004_Taxi/sample.parquet ADDED
Binary file (7.27 kB). View file
 
data/005_NYC/qa.parquet ADDED
Binary file (5.16 kB). View file
 
data/005_NYC/sample.parquet ADDED
Binary file (5.02 kB). View file
 
data/006_London/qa.parquet ADDED
Binary file (5.01 kB). View file
 
data/006_London/sample.parquet ADDED
Binary file (7.06 kB). View file
 
data/007_Fifa/qa.parquet ADDED
Binary file (5.3 kB). View file
 
data/007_Fifa/sample.parquet ADDED
Binary file (7.07 kB). View file
 
data/008_Tornados/qa.parquet ADDED
Binary file (4.67 kB). View file
 
data/008_Tornados/sample.parquet ADDED
Binary file (3.88 kB). View file
 
data/009_Central/qa.parquet ADDED
Binary file (4.64 kB). View file
 
data/009_Central/sample.parquet ADDED
Binary file (2.75 kB). View file
 
data/010_ECommerce/qa.parquet ADDED
Binary file (4.7 kB). View file
 
data/010_ECommerce/sample.parquet ADDED
Binary file (3.98 kB). View file
 
data/011_SF/qa.parquet ADDED
Binary file (5.98 kB). View file
 
data/011_SF/sample.parquet ADDED
Binary file (6.72 kB). View file
 
data/012_Heart/qa.parquet ADDED
Binary file (4.73 kB). View file
 
data/012_Heart/sample.parquet ADDED
Binary file (4.61 kB). View file
 
data/013_Roller/qa.parquet ADDED
Binary file (5.79 kB). View file
 
data/013_Roller/sample.parquet ADDED
Binary file (7.58 kB). View file
 
data/014_Airbnb/qa.parquet ADDED
Binary file (5.74 kB). View file
 
data/014_Airbnb/sample.parquet ADDED
Binary file (74.7 kB). View file
 
data/015_Food/qa.parquet ADDED
Binary file (4.53 kB). View file
 
data/015_Food/sample.parquet ADDED
Binary file (3.04 kB). View file
 
data/016_Holiday/qa.parquet ADDED
Binary file (4.66 kB). View file
 
data/016_Holiday/sample.parquet ADDED
Binary file (9.42 kB). View file
 
data/017_Hacker/qa.parquet ADDED
Binary file (5.06 kB). View file
 
data/017_Hacker/sample.parquet ADDED
Binary file (6.83 kB). View file
 
data/018_Staff/qa.parquet ADDED
Binary file (4.91 kB). View file
 
data/018_Staff/sample.parquet ADDED
Binary file (6.01 kB). View file
 
data/019_Aircraft/qa.parquet ADDED
Binary file (5.61 kB). View file
 
data/019_Aircraft/sample.parquet ADDED
Binary file (9.84 kB). View file
 
data/020_Real/qa.parquet ADDED
Binary file (5 kB). View file
 
data/020_Real/sample.parquet ADDED
Binary file (28.3 kB). View file
 
data/021_Telco/qa.parquet ADDED
Binary file (5.1 kB). View file
 
data/021_Telco/sample.parquet ADDED
Binary file (10.5 kB). View file
 
data/022_Airbnbs/qa.parquet ADDED
Binary file (5.76 kB). View file
 
data/022_Airbnbs/sample.parquet ADDED
Binary file (6.3 kB). View file
 
data/023_Climate/qa.parquet ADDED
Binary file (5.33 kB). View file
 
data/023_Climate/sample.parquet ADDED
Binary file (7.35 kB). View file
 
data/024_Salary/qa.parquet ADDED
Binary file (6.65 kB). View file
 
data/024_Salary/sample.parquet ADDED
Binary file (8.23 kB). View file
 
data/025_Data/qa.parquet ADDED
Binary file (5.19 kB). View file
 
data/025_Data/sample.parquet ADDED
Binary file (3.43 kB). View file