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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
model: string
tag: string
npz: string
classes: list<item: string>
child 0, item: string
nan: int64
all: struct<n: int64, n_land: int64, n_ocean: int64, n_unscored: int64, hit_land: double, ed_land_mean: d (... 324 chars omitted)
child 0, n: int64
child 1, n_land: int64
child 2, n_ocean: int64
child 3, n_unscored: int64
child 4, hit_land: double
child 5, ed_land_mean: double
child 6, ed_land_median: double
child 7, ed_land_p90: double
child 8, land_ocean_rate: double
child 9, ocean_ocean_rate: double
child 10, ed_ocean_mean: double
child 11, ed_ocean_median: double
child 12, ocean_attrib_nearest: double
child 13, ocean_nearest_share_median: double
child 14, ocean_second_share_median: double
child 15, ocean_not_ocean_attrib_nearest: double
child 16, z_median: double
child 17, z_p10: double
by_band: struct<S60-: struct<n: int64, n_land: int64, n_ocean: int64, n_unscored: int64, hit_land: double, ed (... 2506 chars omitted)
child 0, S60-: struct<n: int64, n_land: int64, n_ocean: int64, n_unscored: int64, hit_land: double, ed_land_mean: d (... 324 chars omitted)
child 0, n: int64
child 1, n_land: int64
child 2, n_ocean: int64
child 3, n_unscored: int64
child 4, hit_land: double
child 5, ed_land_mean: double
child 6, ed_land_median: double
child 7, ed_land_p90: double
child 8, land_ocean_rate: double
child 9, ocean_ocean_rate: double
child 10, ed_ocean_mean: double
child
...
ouble
child 5, Oceania: double
child 6, Antarctica: double
child 7, Ocean: double
main_ocean_rate_on_sub2: double
agree_argmax7_ocean: double
n_land: int64
z_nearest_median: double
agree_argmax7_land: double
n_sub2: int64
z_nearest_p10: double
nearest_hit_ocean_main7: double
nearest_hit_ocean_k3b: double
land_hit_k3b: double
tv_median_ocean: double
n_ocean: int64
land_hit_main7: double
by_nearest_km: struct<lt200: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>, 2 (... 284 chars omitted)
child 0, lt200: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
child 1, 200to1000: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
child 2, 1000to3000: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
child 3, ge3000: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
version: string
to
{'model': Value('string'), 'version': Value('string'), 'n_sub2': Value('int64'), 'n_ocean': Value('int64'), 'n_land': Value('int64'), 'z_nearest_median': Value('float64'), 'z_nearest_p10': Value('float64'), 'main_ocean_rate_on_sub2': Value('float64'), 'agree_argmax7_ocean': Value('float64'), 'agree_argmax7_land': Value('float64'), 'nearest_hit_ocean_main7': Value('float64'), 'nearest_hit_ocean_k3b': Value('float64'), 'land_hit_k3b': Value('float64'), 'land_hit_main7': Value('float64'), 'tv_median_ocean': Value('float64'), 'by_nearest_km': {'lt200': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}, '200to1000': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}, '1000to3000': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}, 'ge3000': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
tag: string
npz: string
classes: list<item: string>
child 0, item: string
nan: int64
all: struct<n: int64, n_land: int64, n_ocean: int64, n_unscored: int64, hit_land: double, ed_land_mean: d (... 324 chars omitted)
child 0, n: int64
child 1, n_land: int64
child 2, n_ocean: int64
child 3, n_unscored: int64
child 4, hit_land: double
child 5, ed_land_mean: double
child 6, ed_land_median: double
child 7, ed_land_p90: double
child 8, land_ocean_rate: double
child 9, ocean_ocean_rate: double
child 10, ed_ocean_mean: double
child 11, ed_ocean_median: double
child 12, ocean_attrib_nearest: double
child 13, ocean_nearest_share_median: double
child 14, ocean_second_share_median: double
child 15, ocean_not_ocean_attrib_nearest: double
child 16, z_median: double
child 17, z_p10: double
by_band: struct<S60-: struct<n: int64, n_land: int64, n_ocean: int64, n_unscored: int64, hit_land: double, ed (... 2506 chars omitted)
child 0, S60-: struct<n: int64, n_land: int64, n_ocean: int64, n_unscored: int64, hit_land: double, ed_land_mean: d (... 324 chars omitted)
child 0, n: int64
child 1, n_land: int64
child 2, n_ocean: int64
child 3, n_unscored: int64
child 4, hit_land: double
child 5, ed_land_mean: double
child 6, ed_land_median: double
child 7, ed_land_p90: double
child 8, land_ocean_rate: double
child 9, ocean_ocean_rate: double
child 10, ed_ocean_mean: double
child
...
ouble
child 5, Oceania: double
child 6, Antarctica: double
child 7, Ocean: double
main_ocean_rate_on_sub2: double
agree_argmax7_ocean: double
n_land: int64
z_nearest_median: double
agree_argmax7_land: double
n_sub2: int64
z_nearest_p10: double
nearest_hit_ocean_main7: double
nearest_hit_ocean_k3b: double
land_hit_k3b: double
tv_median_ocean: double
n_ocean: int64
land_hit_main7: double
by_nearest_km: struct<lt200: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>, 2 (... 284 chars omitted)
child 0, lt200: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
child 1, 200to1000: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
child 2, 1000to3000: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
child 3, ge3000: struct<n: int64, agree: double, nearest_hit_main7: double, nearest_hit_k3b: double>
child 0, n: int64
child 1, agree: double
child 2, nearest_hit_main7: double
child 3, nearest_hit_k3b: double
version: string
to
{'model': Value('string'), 'version': Value('string'), 'n_sub2': Value('int64'), 'n_ocean': Value('int64'), 'n_land': Value('int64'), 'z_nearest_median': Value('float64'), 'z_nearest_p10': Value('float64'), 'main_ocean_rate_on_sub2': Value('float64'), 'agree_argmax7_ocean': Value('float64'), 'agree_argmax7_land': Value('float64'), 'nearest_hit_ocean_main7': Value('float64'), 'nearest_hit_ocean_k3b': Value('float64'), 'land_hit_k3b': Value('float64'), 'land_hit_main7': Value('float64'), 'tv_median_ocean': Value('float64'), 'by_nearest_km': {'lt200': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}, '200to1000': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}, '1000to3000': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}, 'ge3000': {'n': Value('int64'), 'agree': Value('float64'), 'nearest_hit_main7': Value('float64'), 'nearest_hit_k3b': Value('float64')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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