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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 3 new columns ({'item', 'brand', 'InStock'}) and 6 missing columns ({'id', 'last_name', 'email', 'gender', 'first_name', 'ip_address'}).

This happened while the csv dataset builder was generating data using

hf://datasets/mmm4/osworld/stock.csv (at revision 7e2fef56b4016f0f0f25e573f17dd11836836e60)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              item: string
              brand: string
              InStock: bool
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 589
              to
              {'id': Value('int64'), 'first_name': Value('string'), 'last_name': Value('string'), 'email': Value('string'), 'gender': Value('string'), 'ip_address': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 3 new columns ({'item', 'brand', 'InStock'}) and 6 missing columns ({'id', 'last_name', 'email', 'gender', 'first_name', 'ip_address'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/mmm4/osworld/stock.csv (at revision 7e2fef56b4016f0f0f25e573f17dd11836836e60)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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id
int64
first_name
string
last_name
string
email
string
gender
string
ip_address
string
1
Salvador
Pauncefoot
Male
130.189.214.196
2
Zorine
Baert
Female
243.208.197.134
3
Hillyer
Cantero
Male
97.216.236.253
4
Maura
Rodda
Female
32.103.114.87
5
Alano
Eastment
Male
106.234.224.218
6
Vaclav
Cradoc
Male
30.109.82.52
7
Juliana
Deverille
Female
16.240.220.135
8
Michaelina
Rickets
Female
205.9.18.1
9
Joann
Abrey
Female
15.245.18.146
10
Hortensia
Birts
Female
249.243.157.17
11
Cristian
Oddey
Male
69.248.181.246
12
Norah
Ayrton
Female
58.196.192.78
13
Kerwinn
Wong
Male
165.120.220.250
14
Percy
Mallalieu
Male
16.141.182.27
15
Rubia
Roughey
Female
94.146.201.127
16
Nerissa
Thomason
Female
72.70.248.7
17
Delbert
Burker
Male
225.12.84.196
18
Ives
Brushneen
Male
96.160.166.145
19
Emelina
Kahan
Female
218.227.246.245
20
Wells
De Cruce
Male
66.223.78.63
21
Kerrill
Lovejoy
Female
60.52.8.17
22
Murdock
Howbrook
Genderqueer
38.163.115.38
23
Batholomew
Mucci
Male
130.25.50.135
24
Mandi
Sleith
Female
226.58.162.221
25
Timothee
Pettigrew
Male
232.65.97.147
26
Conney
Collett
Male
180.57.224.139
27
Tracie
Kisar
Polygender
107.40.213.207
28
Maxy
Ranklin
Male
204.128.31.152
29
Jasmine
Ebbrell
Female
19.2.125.123
30
Lynelle
Waddilove
Female
183.221.70.113
31
Neron
Kivelle
Male
15.173.192.214
32
Huberto
Aidler
Male
141.125.111.64
33
Giffy
Kinner
Male
9.107.132.18
34
Tomasina
Bolderoe
Female
52.38.18.5
35
Jobye
Ramm
Female
191.216.255.62
36
Auguste
Dell
Female
126.126.20.42
37
Wendel
Tunnoch
Male
8.165.62.251
38
Jilleen
Dunning
Female
183.212.253.78
39
Tedman
Boulton
Male
16.126.158.99
40
Nilson
Standing
Male
6.133.87.107
41
Emmett
Degli Abbati
Male
222.93.84.0
42
Ariadne
Royan
Female
222.107.74.60
43
Beatrisa
Matheson
Female
156.174.105.165
44
Myles
Okker
Male
29.185.133.141
45
Hamid
McAneny
Male
194.127.96.97
46
Haroun
Town
Male
195.184.96.221
47
Callida
Esselen
Non-binary
100.231.159.161
48
Julie
Dummett
Male
70.134.170.30
49
Billy
Soame
Male
174.91.125.230
50
Felice
Tolle
Male
57.11.213.74
51
Lyndy
McRannell
Female
27.226.254.173
52
Isidoro
Farre
Male
23.16.67.109
53
Leupold
Kubacki
Male
130.193.101.44
54
Roderick
Hullot
Male
2.86.0.213
55
Brendan
Wignall
Male
108.216.125.56
56
Madelyn
Kilmurry
Female
125.197.56.96
57
Fredrick
Bader
Male
119.197.184.252
58
Conney
Farreil
Male
112.150.194.48
59
Godard
Raatz
Male
236.70.30.68
60
Justis
Wildes
Genderfluid
187.141.19.180
61
Lutero
Sapwell
Male
186.254.195.21
62
Bar
Seargeant
Male
51.172.86.235
63
Lisette
Kalaher
Female
44.174.192.155
64
Clemmy
Chestnut
Male
40.168.89.95
65
Kalila
Desport
Female
84.29.180.38
66
Timmi
Boyn
Female
4.195.184.118
67
Leora
Yitzovitz
Female
218.53.182.72
68
Cord
Croxall
Male
88.8.64.44
69
Boy
Cursons
Male
247.182.183.221
70
Ty
Gerren
Male
71.198.217.95
71
Marketa
Dregan
Female
12.8.125.46
72
Saudra
Toulmin
Female
38.195.60.221
73
Aura
Domoney
Female
28.206.143.150
74
Misti
Lavington
Female
198.95.121.9
75
Vilhelmina
Village
Female
78.166.220.104
76
Gwynne
Burns
Genderqueer
1.184.183.137
77
Amitie
Harper
Female
79.74.227.73
78
Leicester
Ravenhill
Male
237.62.243.177
79
Mae
Laydon
Female
182.187.131.93
80
Rainer
D'Acth
Male
21.230.243.227
81
Celestia
Bayless
Female
186.103.105.19
82
Davy
Musicka
Male
245.145.238.102
83
Arly
Stelljes
Female
104.101.63.214
84
Worthy
Cleynaert
Male
59.16.63.107
85
Roi
Nevill
Male
172.190.34.209
86
Lettie
Dillingham
Female
199.206.213.95
87
Conrad
Blakeman
Male
178.217.158.173
88
Mariya
Courtese
Female
30.58.30.110
89
Robinetta
Gillard
Female
238.0.197.172
90
Marillin
Lindman
Female
137.236.186.158
91
Zacharias
Markus
Male
50.153.56.232
92
Ortensia
Dedmam
Female
184.204.152.123
93
Farand
Lindenberg
Female
130.211.23.14
94
Chester
Bayliss
Male
118.136.182.143
95
Shelley
Haseley
Female
129.218.48.168
96
Alta
Ickovicz
Female
208.152.156.178
97
Nial
Pavelka
Male
84.129.242.6
98
Ian
Mandres
Bigender
222.198.53.205
99
Arlyne
MacMichael
Bigender
168.136.136.68
100
Sadye
McLaverty
Genderqueer
95.222.228.14
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