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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 4 new columns ({'Part of Speech', 'Letters', 'Definition', 'Synonyms'})
This happened while the csv dataset builder was generating data using
hf://datasets/opensporks/word_sample.json/unique_words.csv (at revision 278df9674a4030aba502b882dc285bc96ba465bd)
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 2013, 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
Part of Speech: string
Definition: string
Synonyms: string
Letters: double
count: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 956
to
{'word': Value(dtype='string', id=None), '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 1396, 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 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, 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 1884, 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 2015, 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 4 new columns ({'Part of Speech', 'Letters', 'Definition', 'Synonyms'})
This happened while the csv dataset builder was generating data using
hf://datasets/opensporks/word_sample.json/unique_words.csv (at revision 278df9674a4030aba502b882dc285bc96ba465bd)
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 | count
int64 |
|---|---|
the | 125,971,793,511 |
of | 77,466,218,166 |
and | 55,607,010,885 |
to | 46,622,823,631 |
in | 40,069,476,427 |
a | 35,132,488,627 |
is | 19,364,876,716 |
that | 18,667,136,173 |
for | 18,525,071,087 |
as | 12,871,208,057 |
by | 12,332,619,973 |
be | 12,294,483,149 |
it | 11,994,960,844 |
with | 11,876,159,369 |
on | 11,798,904,606 |
was | 11,265,433,494 |
or | 10,460,165,418 |
not | 10,388,733,666 |
this | 9,343,733,263 |
are | 9,012,560,640 |
i | 8,529,705,082 |
from | 8,398,028,679 |
at | 8,347,148,643 |
he | 7,414,984,637 |
which | 6,837,362,033 |
an | 6,616,825,208 |
have | 6,472,055,759 |
his | 6,321,370,580 |
but | 5,274,289,184 |
you | 5,136,403,520 |
we | 4,806,946,069 |
all | 4,721,453,560 |
were | 4,711,163,000 |
they | 4,674,959,004 |
one | 4,615,625,075 |
had | 4,467,177,865 |
has | 4,295,647,094 |
will | 4,131,695,626 |
their | 4,058,311,467 |
been | 3,833,419,756 |
other | 3,621,031,643 |
if | 3,558,853,549 |
can | 3,504,680,360 |
may | 3,494,071,401 |
there | 3,441,017,212 |
would | 3,426,403,207 |
no | 3,299,635,540 |
more | 3,266,223,769 |
new | 3,191,765,291 |
such | 3,173,369,270 |
its | 3,160,953,963 |
when | 3,127,527,437 |
any | 2,977,738,774 |
these | 2,957,571,963 |
who | 2,920,499,269 |
so | 2,915,805,543 |
her | 2,876,060,797 |
time | 2,844,214,982 |
than | 2,821,118,720 |
do | 2,573,486,964 |
she | 2,473,849,613 |
some | 2,431,463,367 |
what | 2,387,322,337 |
about | 2,356,310,055 |
state | 2,349,552,959 |
only | 2,346,575,330 |
two | 2,320,487,269 |
into | 2,295,957,672 |
also | 2,277,394,724 |
out | 2,247,860,777 |
them | 2,239,759,421 |
our | 2,184,379,901 |
said | 2,144,475,432 |
under | 2,138,741,097 |
first | 2,110,041,394 |
my | 2,087,148,273 |
him | 2,055,993,437 |
up | 2,027,284,788 |
see | 1,998,720,597 |
made | 1,997,447,686 |
should | 1,952,830,829 |
after | 1,839,664,795 |
shall | 1,783,938,547 |
your | 1,726,233,522 |
most | 1,713,230,188 |
could | 1,709,690,152 |
then | 1,697,158,548 |
over | 1,673,016,988 |
each | 1,656,109,353 |
year | 1,638,310,751 |
work | 1,635,348,458 |
states | 1,615,967,473 |
where | 1,601,555,361 |
use | 1,586,311,480 |
years | 1,570,846,149 |
me | 1,567,968,527 |
between | 1,545,114,434 |
those | 1,532,715,465 |
same | 1,530,696,473 |
now | 1,520,107,899 |
End of preview.
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