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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
|
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