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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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