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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 1 new columns ({'text'}) and 5 missing columns ({'Word', 'Dispersion', 'Rank', 'Frequency', 'Part of speech'}).
This happened while the csv dataset builder was generating data using
hf://datasets/rnzandwy/Profanity_Dataset/Profanity Dataset/profane.csv (at revision 9619aea2152c71dba754deb2188305c68c115222)
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 370
to
{'Rank': Value('int64'), 'Word': Value('string'), 'Part of speech': Value('string'), 'Frequency': Value('int64'), 'Dispersion': Value('float64')}
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 1456, 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 1055, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/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 1 new columns ({'text'}) and 5 missing columns ({'Word', 'Dispersion', 'Rank', 'Frequency', 'Part of speech'}).
This happened while the csv dataset builder was generating data using
hf://datasets/rnzandwy/Profanity_Dataset/Profanity Dataset/profane.csv (at revision 9619aea2152c71dba754deb2188305c68c115222)
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)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Rank
int64 | Word
string | Part of speech
string | Frequency
int64 | Dispersion
float64 |
|---|---|---|---|---|
1
|
the
|
a
| 22,038,615
| 0.98
|
2
|
be
|
v
| 12,545,825
| 0.97
|
3
|
and
|
c
| 10,741,073
| 0.99
|
4
|
of
|
i
| 10,343,885
| 0.97
|
5
|
a
|
a
| 10,144,200
| 0.98
|
6
|
in
|
i
| 6,996,437
| 0.98
|
7
|
to
|
t
| 6,332,195
| 0.98
|
8
|
have
|
v
| 4,303,955
| 0.97
|
9
|
to
|
i
| 3,856,916
| 0.99
|
10
|
it
|
p
| 3,872,477
| 0.96
|
11
|
I
|
p
| 3,978,265
| 0.93
|
12
|
that
|
c
| 3,430,996
| 0.97
|
13
|
for
|
i
| 3,281,454
| 0.98
|
14
|
you
|
p
| 3,081,151
| 0.92
|
15
|
he
|
p
| 2,909,254
| 0.94
|
16
|
with
|
i
| 2,683,014
| 0.99
|
17
|
on
|
i
| 2,485,306
| 0.99
|
18
|
do
|
v
| 2,573,587
| 0.95
|
19
|
say
|
v
| 1,915,138
| 0.95
|
20
|
this
|
d
| 1,885,366
| 0.96
|
21
|
they
|
p
| 1,865,580
| 0.96
|
22
|
at
|
i
| 1,767,638
| 0.98
|
23
|
but
|
c
| 1,776,767
| 0.97
|
24
|
we
|
p
| 1,820,935
| 0.94
|
25
|
his
|
a
| 1,801,708
| 0.95
|
26
|
from
|
i
| 1,635,914
| 0.99
|
27
|
that
|
d
| 1,712,406
| 0.94
|
28
|
not
|
x
| 1,638,830
| 0.98
|
29
|
n't
|
x
| 1,619,007
| 0.97
|
30
|
by
|
i
| 1,490,548
| 0.96
|
31
|
she
|
p
| 1,484,869
| 0.91
|
32
|
or
|
c
| 1,379,320
| 0.97
|
33
|
as
|
c
| 1,296,879
| 0.98
|
34
|
what
|
d
| 1,181,023
| 0.94
|
35
|
go
|
v
| 1,151,045
| 0.93
|
36
|
their
|
a
| 1,083,029
| 0.97
|
37
|
can
|
v
| 1,022,775
| 0.98
|
38
|
who
|
p
| 1,018,283
| 0.97
|
39
|
get
|
v
| 992,596
| 0.94
|
40
|
if
|
c
| 933,542
| 0.97
|
41
|
would
|
v
| 925,515
| 0.97
|
42
|
her
|
a
| 969,591
| 0.91
|
43
|
all
|
d
| 892,102
| 0.98
|
44
|
my
|
a
| 919,821
| 0.93
|
45
|
make
|
v
| 857,168
| 0.98
|
46
|
about
|
i
| 874,406
| 0.96
|
47
|
know
|
v
| 892,535
| 0.93
|
48
|
will
|
v
| 824,568
| 0.97
|
49
|
as
|
i
| 829,018
| 0.95
|
50
|
up
|
r
| 795,534
| 0.95
|
51
|
one
|
m
| 768,232
| 0.98
|
52
|
time
|
n
| 764,657
| 0.98
|
53
|
there
|
e
| 784,528
| 0.96
|
54
|
year
|
n
| 769,254
| 0.96
|
55
|
so
|
r
| 756,550
| 0.95
|
56
|
think
|
v
| 772,787
| 0.91
|
57
|
when
|
c
| 678,626
| 0.98
|
58
|
which
|
d
| 685,982
| 0.96
|
59
|
them
|
p
| 677,870
| 0.97
|
60
|
some
|
d
| 674,193
| 0.98
|
61
|
me
|
p
| 709,623
| 0.92
|
62
|
people
|
n
| 691,468
| 0.95
|
63
|
take
|
v
| 670,745
| 0.97
|
64
|
out
|
r
| 678,603
| 0.96
|
65
|
into
|
i
| 668,172
| 0.97
|
66
|
just
|
r
| 677,711
| 0.94
|
67
|
see
|
v
| 663,645
| 0.96
|
68
|
him
|
p
| 677,707
| 0.92
|
69
|
your
|
a
| 659,622
| 0.94
|
70
|
come
|
v
| 628,254
| 0.95
|
71
|
could
|
v
| 617,932
| 0.96
|
72
|
now
|
r
| 605,997
| 0.94
|
73
|
than
|
c
| 579,757
| 0.97
|
74
|
like
|
i
| 568,850
| 0.96
|
75
|
other
|
j
| 547,799
| 0.97
|
76
|
how
|
r
| 538,893
| 0.97
|
77
|
then
|
r
| 543,977
| 0.95
|
78
|
its
|
a
| 539,719
| 0.96
|
79
|
our
|
a
| 525,107
| 0.97
|
80
|
two
|
m
| 511,027
| 0.99
|
81
|
more
|
r
| 517,536
| 0.97
|
82
|
these
|
d
| 513,864
| 0.95
|
83
|
want
|
v
| 514,972
| 0.95
|
84
|
way
|
n
| 470,401
| 0.98
|
85
|
look
|
v
| 491,707
| 0.93
|
86
|
first
|
m
| 463,566
| 0.98
|
87
|
also
|
r
| 464,606
| 0.96
|
88
|
new
|
j
| 435,993
| 0.97
|
89
|
because
|
c
| 438,539
| 0.96
|
90
|
day
|
n
| 432,773
| 0.97
|
91
|
more
|
d
| 420,170
| 0.97
|
92
|
use
|
v
| 420,781
| 0.96
|
93
|
no
|
a
| 402,222
| 0.98
|
94
|
man
|
n
| 409,760
| 0.95
|
95
|
find
|
v
| 395,203
| 0.98
|
96
|
here
|
r
| 412,315
| 0.93
|
97
|
thing
|
n
| 400,724
| 0.94
|
98
|
give
|
v
| 384,503
| 0.98
|
99
|
many
|
d
| 385,348
| 0.97
|
100
|
well
|
r
| 411,776
| 0.91
|
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