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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)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.
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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