Hugging Face's logo

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
xet
Community
3
jorses commited on
Commit
401432d
·
1 Parent(s): 9519b36

Simplify split generation (faster loading times)

Browse files
Files changed (1) hide show
  1. README.md +598 -596
README.md CHANGED
@@ -15,593 +15,591 @@ default: qa
15
  configs:
16
  - config_name: qa
17
  data_files:
18
- - split: full
19
- path:
20
- - data/001_Forbes/qa.parquet
21
- - data/002_Titanic/qa.parquet
22
- - data/003_Love/qa.parquet
23
- - data/004_Taxi/qa.parquet
24
- - data/005_NYC/qa.parquet
25
- - data/006_London/qa.parquet
26
- - data/007_Fifa/qa.parquet
27
- - data/008_Tornados/qa.parquet
28
- - data/009_Central/qa.parquet
29
- - data/010_ECommerce/qa.parquet
30
- - data/011_SF/qa.parquet
31
- - data/012_Heart/qa.parquet
32
- - data/013_Roller/qa.parquet
33
- - data/014_Airbnb/qa.parquet
34
- - data/015_Food/qa.parquet
35
- - data/016_Holiday/qa.parquet
36
- - data/017_Hacker/qa.parquet
37
- - data/018_Staff/qa.parquet
38
- - data/019_Aircraft/qa.parquet
39
- - data/020_Real/qa.parquet
40
- - data/021_Telco/qa.parquet
41
- - data/022_Airbnbs/qa.parquet
42
- - data/023_Climate/qa.parquet
43
- - data/024_Salary/qa.parquet
44
- - data/025_Data/qa.parquet
45
- - data/026_Predicting/qa.parquet
46
- - data/027_Supermarket/qa.parquet
47
- - data/028_Predict/qa.parquet
48
- - data/029_NYTimes/qa.parquet
49
- - data/030_Professionals/qa.parquet
50
- - data/031_Trustpilot/qa.parquet
51
- - data/032_Delicatessen/qa.parquet
52
- - data/033_Employee/qa.parquet
53
- - data/034_World/qa.parquet
54
- - data/035_Billboard/qa.parquet
55
- - data/036_US/qa.parquet
56
- - data/037_Ted/qa.parquet
57
- - data/038_Stroke/qa.parquet
58
- - data/039_Happy/qa.parquet
59
- - data/040_Speed/qa.parquet
60
- - data/041_Airline/qa.parquet
61
- - data/042_Predict/qa.parquet
62
- - data/043_Predict/qa.parquet
63
- - data/044_IMDb/qa.parquet
64
- - data/045_Predict/qa.parquet
65
- - data/046_120/qa.parquet
66
- - data/047_Bank/qa.parquet
67
- - data/048_Data/qa.parquet
68
- - data/049_Boris/qa.parquet
69
- - data/050_ING/qa.parquet
70
- - data/051_Pokemon/qa.parquet
71
- - data/052_Professional/qa.parquet
72
- - data/053_Patents/qa.parquet
73
- - data/054_Joe/qa.parquet
74
- - data/055_German/qa.parquet
75
- - data/056_Emoji/qa.parquet
76
- - data/057_Spain/qa.parquet
77
- - data/058_US/qa.parquet
78
- - data/059_Second/qa.parquet
79
- - data/060_Bakery/qa.parquet
80
- - data/061_Disneyland/qa.parquet
81
- - data/062_Trump/qa.parquet
82
- - data/063_Influencers/qa.parquet
83
- - data/064_Clustering/qa.parquet
84
- - data/065_RFM/qa.parquet
85
- - split: 001_Forbes
86
- path: data/001_Forbes/qa.parquet
87
- - split: 002_Titanic
88
- path: data/002_Titanic/qa.parquet
89
- - split: 003_Love
90
- path: data/003_Love/qa.parquet
91
- - split: 004_Taxi
92
- path: data/004_Taxi/qa.parquet
93
- - split: 005_NYC
94
- path: data/005_NYC/qa.parquet
95
- - split: 006_London
96
- path: data/006_London/qa.parquet
97
- - split: 007_Fifa
98
- path: data/007_Fifa/qa.parquet
99
- - split: 008_Tornados
100
- path: data/008_Tornados/qa.parquet
101
- - split: 009_Central
102
- path: data/009_Central/qa.parquet
103
- - split: 010_ECommerce
104
- path: data/010_ECommerce/qa.parquet
105
- - split: 011_SF
106
- path: data/011_SF/qa.parquet
107
- - split: 012_Heart
108
- path: data/012_Heart/qa.parquet
109
- - split: 013_Roller
110
- path: data/013_Roller/qa.parquet
111
- - split: 014_Airbnb
112
- path: data/014_Airbnb/qa.parquet
113
- - split: 015_Food
114
- path: data/015_Food/qa.parquet
115
- - split: 016_Holiday
116
- path: data/016_Holiday/qa.parquet
117
- - split: 017_Hacker
118
- path: data/017_Hacker/qa.parquet
119
- - split: 018_Staff
120
- path: data/018_Staff/qa.parquet
121
- - split: 019_Aircraft
122
- path: data/019_Aircraft/qa.parquet
123
- - split: 020_Real
124
- path: data/020_Real/qa.parquet
125
- - split: 021_Telco
126
- path: data/021_Telco/qa.parquet
127
- - split: 022_Airbnbs
128
- path: data/022_Airbnbs/qa.parquet
129
- - split: 023_Climate
130
- path: data/023_Climate/qa.parquet
131
- - split: 024_Salary
132
- path: data/024_Salary/qa.parquet
133
- - split: 025_Data
134
- path: data/025_Data/qa.parquet
135
- - split: 026_Predicting
136
- path: data/026_Predicting/qa.parquet
137
- - split: 027_Supermarket
138
- path: data/027_Supermarket/qa.parquet
139
- - split: 028_Predict
140
- path: data/028_Predict/qa.parquet
141
- - split: 029_NYTimes
142
- path: data/029_NYTimes/qa.parquet
143
- - split: 030_Professionals
144
- path: data/030_Professionals/qa.parquet
145
- - split: 031_Trustpilot
146
- path: data/031_Trustpilot/qa.parquet
147
- - split: 032_Delicatessen
148
- path: data/032_Delicatessen/qa.parquet
149
- - split: 033_Employee
150
- path: data/033_Employee/qa.parquet
151
- - split: 034_World
152
- path: data/034_World/qa.parquet
153
- - split: 035_Billboard
154
- path: data/035_Billboard/qa.parquet
155
- - split: 036_US
156
- path: data/036_US/qa.parquet
157
- - split: 037_Ted
158
- path: data/037_Ted/qa.parquet
159
- - split: 038_Stroke
160
- path: data/038_Stroke/qa.parquet
161
- - split: 039_Happy
162
- path: data/039_Happy/qa.parquet
163
- - split: 040_Speed
164
- path: data/040_Speed/qa.parquet
165
- - split: 041_Airline
166
- path: data/041_Airline/qa.parquet
167
- - split: 042_Predict
168
- path: data/042_Predict/qa.parquet
169
- - split: 043_Predict
170
- path: data/043_Predict/qa.parquet
171
- - split: 044_IMDb
172
- path: data/044_IMDb/qa.parquet
173
- - split: 045_Predict
174
- path: data/045_Predict/qa.parquet
175
- - split: "046_120"
176
- path: data/046_120/qa.parquet
177
- - split: 047_Bank
178
- path: data/047_Bank/qa.parquet
179
- - split: 048_Data
180
- path: data/048_Data/qa.parquet
181
- - split: 049_Boris
182
- path: data/049_Boris/qa.parquet
183
- - split: 050_ING
184
- path: data/050_ING/qa.parquet
185
- - split: 051_Pokemon
186
- path: data/051_Pokemon/qa.parquet
187
- - split: 052_Professional
188
- path: data/052_Professional/qa.parquet
189
- - split: 053_Patents
190
- path: data/053_Patents/qa.parquet
191
- - split: 054_Joe
192
- path: data/054_Joe/qa.parquet
193
- - split: 055_German
194
- path: data/055_German/qa.parquet
195
- - split: 056_Emoji
196
- path: data/056_Emoji/qa.parquet
197
- - split: 057_Spain
198
- path: data/057_Spain/qa.parquet
199
- - split: 058_US
200
- path: data/058_US/qa.parquet
201
- - split: 059_Second
202
- path: data/059_Second/qa.parquet
203
- - split: 060_Bakery
204
- path: data/060_Bakery/qa.parquet
205
- - split: 061_Disneyland
206
- path: data/061_Disneyland/qa.parquet
207
- - split: 062_Trump
208
- path: data/062_Trump/qa.parquet
209
- - split: 063_Influencers
210
- path: data/063_Influencers/qa.parquet
211
- - split: 064_Clustering
212
- path: data/064_Clustering/qa.parquet
213
- - split: 065_RFM
214
- path: data/065_RFM/qa.parquet
215
- - config_name: 001_Forbes
216
- data_files:
217
- - split: full
218
- path: data/001_Forbes/all.parquet
219
- - split: lite
220
- path: data/001_Forbes/sample.parquet
221
- - config_name: 002_Titanic
222
- data_files:
223
- - split: full
224
- path: data/002_Titanic/all.parquet
225
- - split: lite
226
- path: data/002_Titanic/sample.parquet
227
- - config_name: 003_Love
228
- data_files:
229
- - split: full
230
- path: data/003_Love/all.parquet
231
- - split: lite
232
- path: data/003_Love/sample.parquet
233
- - config_name: 004_Taxi
234
- data_files:
235
- - split: full
236
- path: data/004_Taxi/all.parquet
237
- - split: lite
238
- path: data/004_Taxi/sample.parquet
239
- - config_name: 005_NYC
240
- data_files:
241
- - split: full
242
- path: data/005_NYC/all.parquet
243
- - split: lite
244
- path: data/005_NYC/sample.parquet
245
- - config_name: 006_London
246
- data_files:
247
- - split: full
248
- path: data/006_London/all.parquet
249
- - split: lite
250
- path: data/006_London/sample.parquet
251
- - config_name: 007_Fifa
252
- data_files:
253
- - split: full
254
- path: data/007_Fifa/all.parquet
255
- - split: lite
256
- path: data/007_Fifa/sample.parquet
257
- - config_name: 008_Tornados
258
- data_files:
259
- - split: full
260
- path: data/008_Tornados/all.parquet
261
- - split: lite
262
- path: data/008_Tornados/sample.parquet
263
- - config_name: 009_Central
264
- data_files:
265
- - split: full
266
- path: data/009_Central/all.parquet
267
- - split: lite
268
- path: data/009_Central/sample.parquet
269
- - config_name: 010_ECommerce
270
- data_files:
271
- - split: full
272
- path: data/010_ECommerce/all.parquet
273
- - split: lite
274
- path: data/010_ECommerce/sample.parquet
275
- - config_name: 011_SF
276
- data_files:
277
- - split: full
278
- path: data/011_SF/all.parquet
279
- - split: lite
280
- path: data/011_SF/sample.parquet
281
- - config_name: 012_Heart
282
- data_files:
283
- - split: full
284
- path: data/012_Heart/all.parquet
285
- - split: lite
286
- path: data/012_Heart/sample.parquet
287
- - config_name: 013_Roller
288
- data_files:
289
- - split: full
290
- path: data/013_Roller/all.parquet
291
- - split: lite
292
- path: data/013_Roller/sample.parquet
293
- - config_name: 014_Airbnb
294
- data_files:
295
- - split: full
296
- path: data/014_Airbnb/all.parquet
297
- - split: lite
298
- path: data/014_Airbnb/sample.parquet
299
- - config_name: 015_Food
300
- data_files:
301
- - split: full
302
- path: data/015_Food/all.parquet
303
- - split: lite
304
- path: data/015_Food/sample.parquet
305
- - config_name: 016_Holiday
306
- data_files:
307
- - split: full
308
- path: data/016_Holiday/all.parquet
309
- - split: lite
310
- path: data/016_Holiday/sample.parquet
311
- - config_name: 017_Hacker
312
- data_files:
313
- - split: full
314
- path: data/017_Hacker/all.parquet
315
- - split: lite
316
- path: data/017_Hacker/sample.parquet
317
- - config_name: 018_Staff
318
- data_files:
319
- - split: full
320
- path: data/018_Staff/all.parquet
321
- - split: lite
322
- path: data/018_Staff/sample.parquet
323
- - config_name: 019_Aircraft
324
- data_files:
325
- - split: full
326
- path: data/019_Aircraft/all.parquet
327
- - split: lite
328
- path: data/019_Aircraft/sample.parquet
329
- - config_name: 020_Real
330
- data_files:
331
- - split: full
332
- path: data/020_Real/all.parquet
333
- - split: lite
334
- path: data/020_Real/sample.parquet
335
- - config_name: 021_Telco
336
- data_files:
337
- - split: full
338
- path: data/021_Telco/all.parquet
339
- - split: lite
340
- path: data/021_Telco/sample.parquet
341
- - config_name: 022_Airbnbs
342
- data_files:
343
- - split: full
344
- path: data/022_Airbnbs/all.parquet
345
- - split: lite
346
- path: data/022_Airbnbs/sample.parquet
347
- - config_name: 023_Climate
348
- data_files:
349
- - split: full
350
- path: data/023_Climate/all.parquet
351
- - split: lite
352
- path: data/023_Climate/sample.parquet
353
- - config_name: 024_Salary
354
- data_files:
355
- - split: full
356
- path: data/024_Salary/all.parquet
357
- - split: lite
358
- path: data/024_Salary/sample.parquet
359
- - config_name: 025_Data
360
- data_files:
361
- - split: full
362
- path: data/025_Data/all.parquet
363
- - split: lite
364
- path: data/025_Data/sample.parquet
365
- - config_name: 026_Predicting
366
- data_files:
367
- - split: full
368
- path: data/026_Predicting/all.parquet
369
- - split: lite
370
- path: data/026_Predicting/sample.parquet
371
- - config_name: 027_Supermarket
372
- data_files:
373
- - split: full
374
- path: data/027_Supermarket/all.parquet
375
- - split: lite
376
- path: data/027_Supermarket/sample.parquet
377
- - config_name: 028_Predict
378
- data_files:
379
- - split: full
380
- path: data/028_Predict/all.parquet
381
- - split: lite
382
- path: data/028_Predict/sample.parquet
383
- - config_name: 029_NYTimes
384
- data_files:
385
- - split: full
386
- path: data/029_NYTimes/all.parquet
387
- - split: lite
388
- path: data/029_NYTimes/sample.parquet
389
- - config_name: 030_Professionals
390
- data_files:
391
- - split: full
392
- path: data/030_Professionals/all.parquet
393
- - split: lite
394
- path: data/030_Professionals/sample.parquet
395
- - config_name: 031_Trustpilot
396
- data_files:
397
- - split: full
398
- path: data/031_Trustpilot/all.parquet
399
- - split: lite
400
- path: data/031_Trustpilot/sample.parquet
401
- - config_name: 032_Delicatessen
402
- data_files:
403
- - split: full
404
- path: data/032_Delicatessen/all.parquet
405
- - split: lite
406
- path: data/032_Delicatessen/sample.parquet
407
- - config_name: 033_Employee
408
- data_files:
409
- - split: full
410
- path: data/033_Employee/all.parquet
411
- - split: lite
412
- path: data/033_Employee/sample.parquet
413
- - config_name: 034_World
414
- data_files:
415
- - split: full
416
- path: data/034_World/all.parquet
417
- - split: lite
418
- path: data/034_World/sample.parquet
419
- - config_name: 035_Billboard
420
- data_files:
421
- - split: full
422
- path: data/035_Billboard/all.parquet
423
- - split: lite
424
- path: data/035_Billboard/sample.parquet
425
- - config_name: 036_US
426
- data_files:
427
- - split: full
428
- path: data/036_US/all.parquet
429
- - split: lite
430
- path: data/036_US/sample.parquet
431
- - config_name: 037_Ted
432
- data_files:
433
- - split: full
434
- path: data/037_Ted/all.parquet
435
- - split: lite
436
- path: data/037_Ted/sample.parquet
437
- - config_name: 038_Stroke
438
- data_files:
439
- - split: full
440
- path: data/038_Stroke/all.parquet
441
- - split: lite
442
- path: data/038_Stroke/sample.parquet
443
- - config_name: 039_Happy
444
- data_files:
445
- - split: full
446
- path: data/039_Happy/all.parquet
447
- - split: lite
448
- path: data/039_Happy/sample.parquet
449
- - config_name: 040_Speed
450
- data_files:
451
- - split: full
452
- path: data/040_Speed/all.parquet
453
- - split: lite
454
- path: data/040_Speed/sample.parquet
455
- - config_name: 041_Airline
456
- data_files:
457
- - split: full
458
- path: data/041_Airline/all.parquet
459
- - split: lite
460
- path: data/041_Airline/sample.parquet
461
- - config_name: 042_Predict
462
- data_files:
463
- - split: full
464
- path: data/042_Predict/all.parquet
465
- - split: lite
466
- path: data/042_Predict/sample.parquet
467
- - config_name: 043_Predict
468
- data_files:
469
- - split: full
470
- path: data/043_Predict/all.parquet
471
- - split: lite
472
- path: data/043_Predict/sample.parquet
473
- - config_name: 044_IMDb
474
- data_files:
475
- - split: full
476
- path: data/044_IMDb/all.parquet
477
- - split: lite
478
- path: data/044_IMDb/sample.parquet
479
- - config_name: 045_Predict
480
- data_files:
481
- - split: full
482
- path: data/045_Predict/all.parquet
483
- - split: lite
484
- path: data/045_Predict/sample.parquet
485
- - config_name: "046_120"
486
- data_files:
487
- - split: full
488
- path: data/046_120/all.parquet
489
- - split: lite
490
- path: data/046_120/sample.parquet
491
- - config_name: 047_Bank
492
- data_files:
493
- - split: full
494
- path: data/047_Bank/all.parquet
495
- - split: lite
496
- path: data/047_Bank/sample.parquet
497
- - config_name: 048_Data
498
- data_files:
499
- - split: full
500
- path: data/048_Data/all.parquet
501
- - split: lite
502
- path: data/048_Data/sample.parquet
503
- - config_name: 049_Boris
504
- data_files:
505
- - split: full
506
- path: data/049_Boris/all.parquet
507
- - split: lite
508
- path: data/049_Boris/sample.parquet
509
- - config_name: 050_ING
510
- data_files:
511
- - split: full
512
- path: data/050_ING/all.parquet
513
- - split: lite
514
- path: data/050_ING/sample.parquet
515
- - config_name: 051_Pokemon
516
- data_files:
517
- - split: full
518
- path: data/051_Pokemon/all.parquet
519
- - split: lite
520
- path: data/051_Pokemon/sample.parquet
521
- - config_name: 052_Professional
522
- data_files:
523
- - split: full
524
- path: data/052_Professional/all.parquet
525
- - split: lite
526
- path: data/052_Professional/sample.parquet
527
- - config_name: 053_Patents
528
- data_files:
529
- - split: full
530
- path: data/053_Patents/all.parquet
531
- - split: lite
532
- path: data/053_Patents/sample.parquet
533
- - config_name: 054_Joe
534
- data_files:
535
- - split: full
536
- path: data/054_Joe/all.parquet
537
- - split: lite
538
- path: data/054_Joe/sample.parquet
539
- - config_name: 055_German
540
- data_files:
541
- - split: full
542
- path: data/055_German/all.parquet
543
- - split: lite
544
- path: data/055_German/sample.parquet
545
- - config_name: 056_Emoji
546
- data_files:
547
- - split: full
548
- path: data/056_Emoji/all.parquet
549
- - split: lite
550
- path: data/056_Emoji/sample.parquet
551
- - config_name: 057_Spain
552
- data_files:
553
- - split: full
554
- path: data/057_Spain/all.parquet
555
- - split: lite
556
- path: data/057_Spain/sample.parquet
557
- - config_name: 058_US
558
- data_files:
559
- - split: full
560
- path: data/058_US/all.parquet
561
- - split: lite
562
- path: data/058_US/sample.parquet
563
- - config_name: 059_Second
564
- data_files:
565
- - split: full
566
- path: data/059_Second/all.parquet
567
- - split: lite
568
- path: data/059_Second/sample.parquet
569
- - config_name: 060_Bakery
570
- data_files:
571
- - split: full
572
- path: data/060_Bakery/all.parquet
573
- - split: lite
574
- path: data/060_Bakery/sample.parquet
575
- - config_name: 061_Disneyland
576
- data_files:
577
- - split: full
578
- path: data/061_Disneyland/all.parquet
579
- - split: lite
580
- path: data/061_Disneyland/sample.parquet
581
- - config_name: 062_Trump
582
- data_files:
583
- - split: full
584
- path: data/062_Trump/all.parquet
585
- - split: lite
586
- path: data/062_Trump/sample.parquet
587
- - config_name: 063_Influencers
588
- data_files:
589
- - split: full
590
- path: data/063_Influencers/all.parquet
591
- - split: lite
592
- path: data/063_Influencers/sample.parquet
593
- - config_name: 064_Clustering
594
- data_files:
595
- - split: full
596
- path: data/064_Clustering/all.parquet
597
- - split: lite
598
- path: data/064_Clustering/sample.parquet
599
- - config_name: 065_RFM
600
- data_files:
601
- - split: full
602
- path: data/065_RFM/all.parquet
603
- - split: lite
604
- path: data/065_RFM/sample.parquet
605
  - config_name: semeval
606
  data_files:
607
  - split: train
@@ -691,24 +689,28 @@ To this end, we provide a corpus of 65 real world datasets, with 3,269,975 and 1
691
  from datasets import load_dataset
692
 
693
  # Load all QA pairs
694
- all_qa = load_dataset("cardiffnlp/databench", name="qa", split="full")
695
 
696
  # Load SemEval 2025 task 8 Question-Answer splits
697
  semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
698
  semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")
 
 
699
 
 
 
 
 
700
 
701
- # "001_Forbes", the id of the dataset where information to answer the Question is located
702
- all_qa['dataset'][0]
703
 
704
- # This id can be used load a specific Question-Answer pair collection from the splits
705
- forbes_qa = load_dataset("cardiffnlp/databench", name="qa", split=all_qa['dataset'][0] )
706
 
707
- # you can load a specific dataset containg the "answer" for a QA pair using the
708
- forbes_full = load_dataset("cardiffnlp/databench", name=all_qa['dataset'][0] , split="full")
709
 
710
- # or to load the databench lite equivalent dataset, to answer the "sample_answer"
711
- forbes_sample = load_dataset("cardiffnlp/databench", name=all_qa['dataset'][0] , split="lite")
712
  ```
713
 
714
  ## 📚 Datasets
 
15
  configs:
16
  - config_name: qa
17
  data_files:
18
+ - data/001_Forbes/qa.parquet
19
+ - data/002_Titanic/qa.parquet
20
+ - data/003_Love/qa.parquet
21
+ - data/004_Taxi/qa.parquet
22
+ - data/005_NYC/qa.parquet
23
+ - data/006_London/qa.parquet
24
+ - data/007_Fifa/qa.parquet
25
+ - data/008_Tornados/qa.parquet
26
+ - data/009_Central/qa.parquet
27
+ - data/010_ECommerce/qa.parquet
28
+ - data/011_SF/qa.parquet
29
+ - data/012_Heart/qa.parquet
30
+ - data/013_Roller/qa.parquet
31
+ - data/014_Airbnb/qa.parquet
32
+ - data/015_Food/qa.parquet
33
+ - data/016_Holiday/qa.parquet
34
+ - data/017_Hacker/qa.parquet
35
+ - data/018_Staff/qa.parquet
36
+ - data/019_Aircraft/qa.parquet
37
+ - data/020_Real/qa.parquet
38
+ - data/021_Telco/qa.parquet
39
+ - data/022_Airbnbs/qa.parquet
40
+ - data/023_Climate/qa.parquet
41
+ - data/024_Salary/qa.parquet
42
+ - data/025_Data/qa.parquet
43
+ - data/026_Predicting/qa.parquet
44
+ - data/027_Supermarket/qa.parquet
45
+ - data/028_Predict/qa.parquet
46
+ - data/029_NYTimes/qa.parquet
47
+ - data/030_Professionals/qa.parquet
48
+ - data/031_Trustpilot/qa.parquet
49
+ - data/032_Delicatessen/qa.parquet
50
+ - data/033_Employee/qa.parquet
51
+ - data/034_World/qa.parquet
52
+ - data/035_Billboard/qa.parquet
53
+ - data/036_US/qa.parquet
54
+ - data/037_Ted/qa.parquet
55
+ - data/038_Stroke/qa.parquet
56
+ - data/039_Happy/qa.parquet
57
+ - data/040_Speed/qa.parquet
58
+ - data/041_Airline/qa.parquet
59
+ - data/042_Predict/qa.parquet
60
+ - data/043_Predict/qa.parquet
61
+ - data/044_IMDb/qa.parquet
62
+ - data/045_Predict/qa.parquet
63
+ - data/046_120/qa.parquet
64
+ - data/047_Bank/qa.parquet
65
+ - data/048_Data/qa.parquet
66
+ - data/049_Boris/qa.parquet
67
+ - data/050_ING/qa.parquet
68
+ - data/051_Pokemon/qa.parquet
69
+ - data/052_Professional/qa.parquet
70
+ - data/053_Patents/qa.parquet
71
+ - data/054_Joe/qa.parquet
72
+ - data/055_German/qa.parquet
73
+ - data/056_Emoji/qa.parquet
74
+ - data/057_Spain/qa.parquet
75
+ - data/058_US/qa.parquet
76
+ - data/059_Second/qa.parquet
77
+ - data/060_Bakery/qa.parquet
78
+ - data/061_Disneyland/qa.parquet
79
+ - data/062_Trump/qa.parquet
80
+ - data/063_Influencers/qa.parquet
81
+ - data/064_Clustering/qa.parquet
82
+ - data/065_RFM/qa.parquet
83
+ # - split: 001_Forbes
84
+ # path: data/001_Forbes/qa.parquet
85
+ # - split: 002_Titanic
86
+ # path: data/002_Titanic/qa.parquet
87
+ # - split: 003_Love
88
+ # path: data/003_Love/qa.parquet
89
+ # - split: 004_Taxi
90
+ # path: data/004_Taxi/qa.parquet
91
+ # - split: 005_NYC
92
+ # path: data/005_NYC/qa.parquet
93
+ # - split: 006_London
94
+ # path: data/006_London/qa.parquet
95
+ # - split: 007_Fifa
96
+ # path: data/007_Fifa/qa.parquet
97
+ # - split: 008_Tornados
98
+ # path: data/008_Tornados/qa.parquet
99
+ # - split: 009_Central
100
+ # path: data/009_Central/qa.parquet
101
+ # - split: 010_ECommerce
102
+ # path: data/010_ECommerce/qa.parquet
103
+ # - split: 011_SF
104
+ # path: data/011_SF/qa.parquet
105
+ # - split: 012_Heart
106
+ # path: data/012_Heart/qa.parquet
107
+ # - split: 013_Roller
108
+ # path: data/013_Roller/qa.parquet
109
+ # - split: 014_Airbnb
110
+ # path: data/014_Airbnb/qa.parquet
111
+ # - split: 015_Food
112
+ # path: data/015_Food/qa.parquet
113
+ # - split: 016_Holiday
114
+ # path: data/016_Holiday/qa.parquet
115
+ # - split: 017_Hacker
116
+ # path: data/017_Hacker/qa.parquet
117
+ # - split: 018_Staff
118
+ # path: data/018_Staff/qa.parquet
119
+ # - split: 019_Aircraft
120
+ # path: data/019_Aircraft/qa.parquet
121
+ # - split: 020_Real
122
+ # path: data/020_Real/qa.parquet
123
+ # - split: 021_Telco
124
+ # path: data/021_Telco/qa.parquet
125
+ # - split: 022_Airbnbs
126
+ # path: data/022_Airbnbs/qa.parquet
127
+ # - split: 023_Climate
128
+ # path: data/023_Climate/qa.parquet
129
+ # - split: 024_Salary
130
+ # path: data/024_Salary/qa.parquet
131
+ # - split: 025_Data
132
+ # path: data/025_Data/qa.parquet
133
+ # - split: 026_Predicting
134
+ # path: data/026_Predicting/qa.parquet
135
+ # - split: 027_Supermarket
136
+ # path: data/027_Supermarket/qa.parquet
137
+ # - split: 028_Predict
138
+ # path: data/028_Predict/qa.parquet
139
+ # - split: 029_NYTimes
140
+ # path: data/029_NYTimes/qa.parquet
141
+ # - split: 030_Professionals
142
+ # path: data/030_Professionals/qa.parquet
143
+ # - split: 031_Trustpilot
144
+ # path: data/031_Trustpilot/qa.parquet
145
+ # - split: 032_Delicatessen
146
+ # path: data/032_Delicatessen/qa.parquet
147
+ # - split: 033_Employee
148
+ # path: data/033_Employee/qa.parquet
149
+ # - split: 034_World
150
+ # path: data/034_World/qa.parquet
151
+ # - split: 035_Billboard
152
+ # path: data/035_Billboard/qa.parquet
153
+ # - split: 036_US
154
+ # path: data/036_US/qa.parquet
155
+ # - split: 037_Ted
156
+ # path: data/037_Ted/qa.parquet
157
+ # - split: 038_Stroke
158
+ # path: data/038_Stroke/qa.parquet
159
+ # - split: 039_Happy
160
+ # path: data/039_Happy/qa.parquet
161
+ # - split: 040_Speed
162
+ # path: data/040_Speed/qa.parquet
163
+ # - split: 041_Airline
164
+ # path: data/041_Airline/qa.parquet
165
+ # - split: 042_Predict
166
+ # path: data/042_Predict/qa.parquet
167
+ # - split: 043_Predict
168
+ # path: data/043_Predict/qa.parquet
169
+ # - split: 044_IMDb
170
+ # path: data/044_IMDb/qa.parquet
171
+ # - split: 045_Predict
172
+ # path: data/045_Predict/qa.parquet
173
+ # - split: "046_120"
174
+ # path: data/046_120/qa.parquet
175
+ # - split: 047_Bank
176
+ # path: data/047_Bank/qa.parquet
177
+ # - split: 048_Data
178
+ # path: data/048_Data/qa.parquet
179
+ # - split: 049_Boris
180
+ # path: data/049_Boris/qa.parquet
181
+ # - split: 050_ING
182
+ # path: data/050_ING/qa.parquet
183
+ # - split: 051_Pokemon
184
+ # path: data/051_Pokemon/qa.parquet
185
+ # - split: 052_Professional
186
+ # path: data/052_Professional/qa.parquet
187
+ # - split: 053_Patents
188
+ # path: data/053_Patents/qa.parquet
189
+ # - split: 054_Joe
190
+ # path: data/054_Joe/qa.parquet
191
+ # - split: 055_German
192
+ # path: data/055_German/qa.parquet
193
+ # - split: 056_Emoji
194
+ # path: data/056_Emoji/qa.parquet
195
+ # - split: 057_Spain
196
+ # path: data/057_Spain/qa.parquet
197
+ # - split: 058_US
198
+ # path: data/058_US/qa.parquet
199
+ # - split: 059_Second
200
+ # path: data/059_Second/qa.parquet
201
+ # - split: 060_Bakery
202
+ # path: data/060_Bakery/qa.parquet
203
+ # - split: 061_Disneyland
204
+ # path: data/061_Disneyland/qa.parquet
205
+ # - split: 062_Trump
206
+ # path: data/062_Trump/qa.parquet
207
+ # - split: 063_Influencers
208
+ # path: data/063_Influencers/qa.parquet
209
+ # - split: 064_Clustering
210
+ # path: data/064_Clustering/qa.parquet
211
+ # - split: 065_RFM
212
+ # path: data/065_RFM/qa.parquet
213
+ # - config_name: 001_Forbes
214
+ # data_files:
215
+ # - split: full
216
+ # path: data/001_Forbes/all.parquet
217
+ # - split: lite
218
+ # path: data/001_Forbes/sample.parquet
219
+ # - config_name: 002_Titanic
220
+ # data_files:
221
+ # - split: full
222
+ # path: data/002_Titanic/all.parquet
223
+ # - split: lite
224
+ # path: data/002_Titanic/sample.parquet
225
+ # - config_name: 003_Love
226
+ # data_files:
227
+ # - split: full
228
+ # path: data/003_Love/all.parquet
229
+ # - split: lite
230
+ # path: data/003_Love/sample.parquet
231
+ # - config_name: 004_Taxi
232
+ # data_files:
233
+ # - split: full
234
+ # path: data/004_Taxi/all.parquet
235
+ # - split: lite
236
+ # path: data/004_Taxi/sample.parquet
237
+ # - config_name: 005_NYC
238
+ # data_files:
239
+ # - split: full
240
+ # path: data/005_NYC/all.parquet
241
+ # - split: lite
242
+ # path: data/005_NYC/sample.parquet
243
+ # - config_name: 006_London
244
+ # data_files:
245
+ # - split: full
246
+ # path: data/006_London/all.parquet
247
+ # - split: lite
248
+ # path: data/006_London/sample.parquet
249
+ # - config_name: 007_Fifa
250
+ # data_files:
251
+ # - split: full
252
+ # path: data/007_Fifa/all.parquet
253
+ # - split: lite
254
+ # path: data/007_Fifa/sample.parquet
255
+ # - config_name: 008_Tornados
256
+ # data_files:
257
+ # - split: full
258
+ # path: data/008_Tornados/all.parquet
259
+ # - split: lite
260
+ # path: data/008_Tornados/sample.parquet
261
+ # - config_name: 009_Central
262
+ # data_files:
263
+ # - split: full
264
+ # path: data/009_Central/all.parquet
265
+ # - split: lite
266
+ # path: data/009_Central/sample.parquet
267
+ # - config_name: 010_ECommerce
268
+ # data_files:
269
+ # - split: full
270
+ # path: data/010_ECommerce/all.parquet
271
+ # - split: lite
272
+ # path: data/010_ECommerce/sample.parquet
273
+ # - config_name: 011_SF
274
+ # data_files:
275
+ # - split: full
276
+ # path: data/011_SF/all.parquet
277
+ # - split: lite
278
+ # path: data/011_SF/sample.parquet
279
+ # - config_name: 012_Heart
280
+ # data_files:
281
+ # - split: full
282
+ # path: data/012_Heart/all.parquet
283
+ # - split: lite
284
+ # path: data/012_Heart/sample.parquet
285
+ # - config_name: 013_Roller
286
+ # data_files:
287
+ # - split: full
288
+ # path: data/013_Roller/all.parquet
289
+ # - split: lite
290
+ # path: data/013_Roller/sample.parquet
291
+ # - config_name: 014_Airbnb
292
+ # data_files:
293
+ # - split: full
294
+ # path: data/014_Airbnb/all.parquet
295
+ # - split: lite
296
+ # path: data/014_Airbnb/sample.parquet
297
+ # - config_name: 015_Food
298
+ # data_files:
299
+ # - split: full
300
+ # path: data/015_Food/all.parquet
301
+ # - split: lite
302
+ # path: data/015_Food/sample.parquet
303
+ # - config_name: 016_Holiday
304
+ # data_files:
305
+ # - split: full
306
+ # path: data/016_Holiday/all.parquet
307
+ # - split: lite
308
+ # path: data/016_Holiday/sample.parquet
309
+ # - config_name: 017_Hacker
310
+ # data_files:
311
+ # - split: full
312
+ # path: data/017_Hacker/all.parquet
313
+ # - split: lite
314
+ # path: data/017_Hacker/sample.parquet
315
+ # - config_name: 018_Staff
316
+ # data_files:
317
+ # - split: full
318
+ # path: data/018_Staff/all.parquet
319
+ # - split: lite
320
+ # path: data/018_Staff/sample.parquet
321
+ # - config_name: 019_Aircraft
322
+ # data_files:
323
+ # - split: full
324
+ # path: data/019_Aircraft/all.parquet
325
+ # - split: lite
326
+ # path: data/019_Aircraft/sample.parquet
327
+ # - config_name: 020_Real
328
+ # data_files:
329
+ # - split: full
330
+ # path: data/020_Real/all.parquet
331
+ # - split: lite
332
+ # path: data/020_Real/sample.parquet
333
+ # - config_name: 021_Telco
334
+ # data_files:
335
+ # - split: full
336
+ # path: data/021_Telco/all.parquet
337
+ # - split: lite
338
+ # path: data/021_Telco/sample.parquet
339
+ # - config_name: 022_Airbnbs
340
+ # data_files:
341
+ # - split: full
342
+ # path: data/022_Airbnbs/all.parquet
343
+ # - split: lite
344
+ # path: data/022_Airbnbs/sample.parquet
345
+ # - config_name: 023_Climate
346
+ # data_files:
347
+ # - split: full
348
+ # path: data/023_Climate/all.parquet
349
+ # - split: lite
350
+ # path: data/023_Climate/sample.parquet
351
+ # - config_name: 024_Salary
352
+ # data_files:
353
+ # - split: full
354
+ # path: data/024_Salary/all.parquet
355
+ # - split: lite
356
+ # path: data/024_Salary/sample.parquet
357
+ # - config_name: 025_Data
358
+ # data_files:
359
+ # - split: full
360
+ # path: data/025_Data/all.parquet
361
+ # - split: lite
362
+ # path: data/025_Data/sample.parquet
363
+ # - config_name: 026_Predicting
364
+ # data_files:
365
+ # - split: full
366
+ # path: data/026_Predicting/all.parquet
367
+ # - split: lite
368
+ # path: data/026_Predicting/sample.parquet
369
+ # - config_name: 027_Supermarket
370
+ # data_files:
371
+ # - split: full
372
+ # path: data/027_Supermarket/all.parquet
373
+ # - split: lite
374
+ # path: data/027_Supermarket/sample.parquet
375
+ # - config_name: 028_Predict
376
+ # data_files:
377
+ # - split: full
378
+ # path: data/028_Predict/all.parquet
379
+ # - split: lite
380
+ # path: data/028_Predict/sample.parquet
381
+ # - config_name: 029_NYTimes
382
+ # data_files:
383
+ # - split: full
384
+ # path: data/029_NYTimes/all.parquet
385
+ # - split: lite
386
+ # path: data/029_NYTimes/sample.parquet
387
+ # - config_name: 030_Professionals
388
+ # data_files:
389
+ # - split: full
390
+ # path: data/030_Professionals/all.parquet
391
+ # - split: lite
392
+ # path: data/030_Professionals/sample.parquet
393
+ # - config_name: 031_Trustpilot
394
+ # data_files:
395
+ # - split: full
396
+ # path: data/031_Trustpilot/all.parquet
397
+ # - split: lite
398
+ # path: data/031_Trustpilot/sample.parquet
399
+ # - config_name: 032_Delicatessen
400
+ # data_files:
401
+ # - split: full
402
+ # path: data/032_Delicatessen/all.parquet
403
+ # - split: lite
404
+ # path: data/032_Delicatessen/sample.parquet
405
+ # - config_name: 033_Employee
406
+ # data_files:
407
+ # - split: full
408
+ # path: data/033_Employee/all.parquet
409
+ # - split: lite
410
+ # path: data/033_Employee/sample.parquet
411
+ # - config_name: 034_World
412
+ # data_files:
413
+ # - split: full
414
+ # path: data/034_World/all.parquet
415
+ # - split: lite
416
+ # path: data/034_World/sample.parquet
417
+ # - config_name: 035_Billboard
418
+ # data_files:
419
+ # - split: full
420
+ # path: data/035_Billboard/all.parquet
421
+ # - split: lite
422
+ # path: data/035_Billboard/sample.parquet
423
+ # - config_name: 036_US
424
+ # data_files:
425
+ # - split: full
426
+ # path: data/036_US/all.parquet
427
+ # - split: lite
428
+ # path: data/036_US/sample.parquet
429
+ # - config_name: 037_Ted
430
+ # data_files:
431
+ # - split: full
432
+ # path: data/037_Ted/all.parquet
433
+ # - split: lite
434
+ # path: data/037_Ted/sample.parquet
435
+ # - config_name: 038_Stroke
436
+ # data_files:
437
+ # - split: full
438
+ # path: data/038_Stroke/all.parquet
439
+ # - split: lite
440
+ # path: data/038_Stroke/sample.parquet
441
+ # - config_name: 039_Happy
442
+ # data_files:
443
+ # - split: full
444
+ # path: data/039_Happy/all.parquet
445
+ # - split: lite
446
+ # path: data/039_Happy/sample.parquet
447
+ # - config_name: 040_Speed
448
+ # data_files:
449
+ # - split: full
450
+ # path: data/040_Speed/all.parquet
451
+ # - split: lite
452
+ # path: data/040_Speed/sample.parquet
453
+ # - config_name: 041_Airline
454
+ # data_files:
455
+ # - split: full
456
+ # path: data/041_Airline/all.parquet
457
+ # - split: lite
458
+ # path: data/041_Airline/sample.parquet
459
+ # - config_name: 042_Predict
460
+ # data_files:
461
+ # - split: full
462
+ # path: data/042_Predict/all.parquet
463
+ # - split: lite
464
+ # path: data/042_Predict/sample.parquet
465
+ # - config_name: 043_Predict
466
+ # data_files:
467
+ # - split: full
468
+ # path: data/043_Predict/all.parquet
469
+ # - split: lite
470
+ # path: data/043_Predict/sample.parquet
471
+ # - config_name: 044_IMDb
472
+ # data_files:
473
+ # - split: full
474
+ # path: data/044_IMDb/all.parquet
475
+ # - split: lite
476
+ # path: data/044_IMDb/sample.parquet
477
+ # - config_name: 045_Predict
478
+ # data_files:
479
+ # - split: full
480
+ # path: data/045_Predict/all.parquet
481
+ # - split: lite
482
+ # path: data/045_Predict/sample.parquet
483
+ # - config_name: "046_120"
484
+ # data_files:
485
+ # - split: full
486
+ # path: data/046_120/all.parquet
487
+ # - split: lite
488
+ # path: data/046_120/sample.parquet
489
+ # - config_name: 047_Bank
490
+ # data_files:
491
+ # - split: full
492
+ # path: data/047_Bank/all.parquet
493
+ # - split: lite
494
+ # path: data/047_Bank/sample.parquet
495
+ # - config_name: 048_Data
496
+ # data_files:
497
+ # - split: full
498
+ # path: data/048_Data/all.parquet
499
+ # - split: lite
500
+ # path: data/048_Data/sample.parquet
501
+ # - config_name: 049_Boris
502
+ # data_files:
503
+ # - split: full
504
+ # path: data/049_Boris/all.parquet
505
+ # - split: lite
506
+ # path: data/049_Boris/sample.parquet
507
+ # - config_name: 050_ING
508
+ # data_files:
509
+ # - split: full
510
+ # path: data/050_ING/all.parquet
511
+ # - split: lite
512
+ # path: data/050_ING/sample.parquet
513
+ # - config_name: 051_Pokemon
514
+ # data_files:
515
+ # - split: full
516
+ # path: data/051_Pokemon/all.parquet
517
+ # - split: lite
518
+ # path: data/051_Pokemon/sample.parquet
519
+ # - config_name: 052_Professional
520
+ # data_files:
521
+ # - split: full
522
+ # path: data/052_Professional/all.parquet
523
+ # - split: lite
524
+ # path: data/052_Professional/sample.parquet
525
+ # - config_name: 053_Patents
526
+ # data_files:
527
+ # - split: full
528
+ # path: data/053_Patents/all.parquet
529
+ # - split: lite
530
+ # path: data/053_Patents/sample.parquet
531
+ # - config_name: 054_Joe
532
+ # data_files:
533
+ # - split: full
534
+ # path: data/054_Joe/all.parquet
535
+ # - split: lite
536
+ # path: data/054_Joe/sample.parquet
537
+ # - config_name: 055_German
538
+ # data_files:
539
+ # - split: full
540
+ # path: data/055_German/all.parquet
541
+ # - split: lite
542
+ # path: data/055_German/sample.parquet
543
+ # - config_name: 056_Emoji
544
+ # data_files:
545
+ # - split: full
546
+ # path: data/056_Emoji/all.parquet
547
+ # - split: lite
548
+ # path: data/056_Emoji/sample.parquet
549
+ # - config_name: 057_Spain
550
+ # data_files:
551
+ # - split: full
552
+ # path: data/057_Spain/all.parquet
553
+ # - split: lite
554
+ # path: data/057_Spain/sample.parquet
555
+ # - config_name: 058_US
556
+ # data_files:
557
+ # - split: full
558
+ # path: data/058_US/all.parquet
559
+ # - split: lite
560
+ # path: data/058_US/sample.parquet
561
+ # - config_name: 059_Second
562
+ # data_files:
563
+ # - split: full
564
+ # path: data/059_Second/all.parquet
565
+ # - split: lite
566
+ # path: data/059_Second/sample.parquet
567
+ # - config_name: 060_Bakery
568
+ # data_files:
569
+ # - split: full
570
+ # path: data/060_Bakery/all.parquet
571
+ # - split: lite
572
+ # path: data/060_Bakery/sample.parquet
573
+ # - config_name: 061_Disneyland
574
+ # data_files:
575
+ # - split: full
576
+ # path: data/061_Disneyland/all.parquet
577
+ # - split: lite
578
+ # path: data/061_Disneyland/sample.parquet
579
+ # - config_name: 062_Trump
580
+ # data_files:
581
+ # - split: full
582
+ # path: data/062_Trump/all.parquet
583
+ # - split: lite
584
+ # path: data/062_Trump/sample.parquet
585
+ # - config_name: 063_Influencers
586
+ # data_files:
587
+ # - split: full
588
+ # path: data/063_Influencers/all.parquet
589
+ # - split: lite
590
+ # path: data/063_Influencers/sample.parquet
591
+ # - config_name: 064_Clustering
592
+ # data_files:
593
+ # - split: full
594
+ # path: data/064_Clustering/all.parquet
595
+ # - split: lite
596
+ # path: data/064_Clustering/sample.parquet
597
+ # - config_name: 065_RFM
598
+ # data_files:
599
+ # - split: full
600
+ # path: data/065_RFM/all.parquet
601
+ # - split: lite
602
+ # path: data/065_RFM/sample.parquet
 
 
603
  - config_name: semeval
604
  data_files:
605
  - split: train
 
689
  from datasets import load_dataset
690
 
691
  # Load all QA pairs
692
+ all_qa = load_dataset("cardiffnlp/databench", name="qa")
693
 
694
  # Load SemEval 2025 task 8 Question-Answer splits
695
  semeval_train_qa = load_dataset("cardiffnlp/databench", name="semeval", split="train")
696
  semeval_dev_qa = load_dataset("cardiffnlp/databench", name="semeval", split="dev")
697
+ ```
698
+ You can use any of the individual [integrated libraries](https://huggingface.co/docs/hub/datasets-libraries#libraries) to load the actual data where the answer is to be retrieved.
699
 
700
+ For example, using pandas in Python:
701
+
702
+ ```python
703
+ import pandas as pd
704
 
705
+ # "001_Forbes", the id of the dataset
706
+ ds_id = all_qa['dataset'][0]
707
 
708
+ # full dataset
709
+ df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/all.parquet")
710
 
711
+ # sample dataset
712
+ df = pd.read_parquet(f"hf://datasets/cardiffnlp/databench/data/{ds_id}/sample.parquet")
713
 
 
 
714
  ```
715
 
716
  ## 📚 Datasets