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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 2 new columns ({'her-count', 'her-npmi'}) and 2 missing columns ({'he-npmi', 'he-count'}).

This happened while the csv dataset builder was generating data using

hf://datasets/datameasurements/hate_speech18_default_train_text/pmi_files/gay-her_npmi.csv (at revision aa84d3ddeb8d41c04d92d5e1bcf157d64aa26c5b)

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 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, 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 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              word: string
              npmi-bias: double
              gay-npmi: double
              gay-count: int64
              her-npmi: double
              her-count: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 952
              to
              {'word': Value(dtype='string', id=None), 'npmi-bias': Value(dtype='float64', id=None), 'gay-npmi': Value(dtype='float64', id=None), 'gay-count': Value(dtype='int64', id=None), 'he-npmi': Value(dtype='float64', id=None), 'he-count': Value(dtype='int64', id=None)}
              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 1321, 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 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, 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 1882, 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 2013, 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 2 new columns ({'her-count', 'her-npmi'}) and 2 missing columns ({'he-npmi', 'he-count'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/datameasurements/hate_speech18_default_train_text/pmi_files/gay-her_npmi.csv (at revision aa84d3ddeb8d41c04d92d5e1bcf157d64aa26c5b)
              
              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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word
string
npmi-bias
float64
gay-npmi
float64
gay-count
int64
he-npmi
float64
he-count
int64
he
-0.371553
0.280825
2
0.652378
292
like
-0.118451
0.158121
1
0.276572
30
i
-0.107759
0.172684
4
0.280443
83
his
-0.093547
0.308349
2
0.401896
45
about
-0.087153
0.180815
1
0.267967
23
was
-0.075486
0.294454
4
0.36994
69
me
-0.071677
0.184634
1
0.256311
20
would
-0.066086
0.187246
1
0.253332
19
what
-0.065923
0.196795
1
0.262718
19
should
-0.065256
0.229006
1
0.294262
19
a
-0.061253
0.318528
10
0.379781
139
be
-0.047804
0.222564
2
0.270369
30
an
-0.047734
0.204166
1
0.2519
16
back
-0.040905
0.22923
1
0.270135
15
if
-0.034093
0.277935
3
0.312028
39
and
-0.024373
0.342611
12
0.366984
134
right
-0.018822
0.260367
1
0.279189
12
school
-0.018741
0.227021
1
0.245762
12
has
-0.017234
0.283411
2
0.300645
23
too
-0.010384
0.237828
1
0.248211
11
time
-0.010281
0.22467
1
0.234951
11
so
-0.010036
0.286997
3
0.297033
32
one
-0.007103
0.256348
2
0.263451
21
out
-0.006974
0.244255
2
0.25123
21
to
-0.00222
0.349255
13
0.351475
128
not
-0.002185
0.27139
3
0.273576
30
man
-0.001659
0.267199
1
0.268858
10
here
-0.000753
0.194259
1
0.195012
10
but
0.005992
0.269636
3
0.263644
28
the
0.007274
0.366925
17
0.359651
159
down
0.008393
0.247098
1
0.238705
9
years
0.00848
0.24203
1
0.23355
9
day
0.008529
0.239113
1
0.230583
9
white
0.010894
0.276618
4
0.265724
36
have
0.011147
0.26556
4
0.254413
36
all
0.01471
0.266387
3
0.251676
26
s
0.017704
0.352112
7
0.334408
60
negro
0.018448
0.280104
1
0.261655
8
off
0.018927
0.258944
1
0.240018
8
on
0.018941
0.319819
6
0.300878
51
country
0.019048
0.253553
1
0.234505
8
around
0.0193
0.242301
1
0.223001
8
very
0.019383
0.238596
1
0.219213
8
now
0.019456
0.235328
1
0.215873
8
think
0.019689
0.22488
1
0.205191
8
how
0.021183
0.28683
2
0.265647
16
we
0.022642
0.230826
2
0.208184
16
at
0.023375
0.285541
3
0.262165
24
then
0.027532
0.295048
2
0.267515
15
or
0.028411
0.277935
3
0.249524
23
it
0.02873
0.311406
7
0.282676
56
seen
0.030388
0.275797
1
0.245409
7
nothing
0.030428
0.274428
1
0.244
7
jews
0.030672
0.266007
1
0.235335
7
ever
0.030771
0.262562
1
0.231792
7
home
0.030844
0.260008
1
0.229164
7
new
0.031787
0.226372
1
0.194585
7
when
0.032256
0.310857
3
0.278601
22
our
0.032549
0.198096
1
0.165547
7
say
0.034258
0.301365
2
0.267107
14
by
0.035453
0.266177
2
0.230724
14
my
0.038712
0.323664
5
0.284952
36
wants
0.041953
0.324289
1
0.282337
6
for
0.042036
0.310272
6
0.268236
43
no
0.042901
0.267291
2
0.22439
13
much
0.044549
0.254202
1
0.209653
6
own
0.044561
0.253877
1
0.209316
6
same
0.044713
0.249482
1
0.204769
6
over
0.044902
0.243955
1
0.199053
6
many
0.045118
0.237573
1
0.192455
6
old
0.049219
0.306594
2
0.257375
12
with
0.050099
0.363872
7
0.313773
47
will
0.055073
0.286023
3
0.23095
18
you
0.055327
0.279437
5
0.22411
32
that
0.055708
0.35857
9
0.302862
60
wrong
0.057365
0.317117
1
0.259752
5
as
0.058252
0.30561
4
0.247358
24
racist
0.05853
0.292177
1
0.233647
5
better
0.058638
0.289788
1
0.23115
5
god
0.058791
0.286379
1
0.227587
5
because
0.064393
0.343565
3
0.279173
16
in
0.068494
0.375308
12
0.306814
76
mr
0.073565
0.347569
1
0.274005
4
brother
0.074709
0.32888
1
0.254171
4
behind
0.075289
0.319068
1
0.243779
4
party
0.075458
0.316168
1
0.24071
4
teacher
0.075668
0.31253
1
0.236862
4
care
0.075863
0.309122
1
0.233259
4
even
0.07628
0.310942
2
0.234662
9
men
0.076528
0.297278
1
0.22075
4
brown
0.076906
0.290376
1
0.21347
4
thing
0.078204
0.265615
1
0.18741
4
watch
0.078805
0.253553
1
0.174748
4
look
0.078973
0.250092
1
0.171118
4
those
0.079729
0.234113
1
0.154384
4
any
0.079901
0.230361
1
0.15046
4
were
0.08013
0.225303
1
0.145173
4
know
0.088628
0.286646
2
0.198018
8
who
0.093741
0.341073
4
0.247332
17
apparently
0.09511
0.355134
1
0.260024
3
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