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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
train: list<item: string>
child 0, item: string
val: list<item: string>
child 0, item: string
test: list<item: string>
child 0, item: string
bounding_box_extent_angstrom: struct<side_min: double, side_max: double, diagonal_min: double, diagonal_max: double>
child 0, side_min: double
child 1, side_max: double
child 2, diagonal_min: double
child 3, diagonal_max: double
atoms_per_structure: struct<min: int64, max: int64, mean: double>
child 0, min: int64
child 1, max: int64
child 2, mean: double
exact: struct<num_complexes: int64, unique_pdb_ids: int64, components: struct<protein: struct<total_atoms: (... 5760 chars omitted)
child 0, num_complexes: int64
child 1, unique_pdb_ids: int64
child 2, components: struct<protein: struct<total_atoms: int64, min: int64, mean: double, max: int64, zero_length: int64> (... 189 chars omitted)
child 0, protein: struct<total_atoms: int64, min: int64, mean: double, max: int64, zero_length: int64>
child 0, total_atoms: int64
child 1, min: int64
child 2, mean: double
child 3, max: int64
child 4, zero_length: int64
child 1, pocket: struct<total_atoms: int64, min: int64, mean: double, max: int64, zero_length: int64>
child 0, total_atoms: int64
child 1, min: int64
child 2, mean: double
child 3, max: int64
child 4, zero_length: int64
child 2, ligand: struct<total_atoms: int64, min: int64, mean: double, max: in
...
child 1, v2_true: int64
child 2, lost: int64
child 3, gained: int64
child 4, z_of_lost: list<item: int64>
child 0, item: int64
child 5, lost_residues: int64
child 6, v1_missing_fields: list<item: string>
child 0, item: string
sample_size: int64
elements: struct<count: int64, atomic_numbers: list<item: int64>, symbols: list<item: string>, counts: struct< (... 248 chars omitted)
child 0, count: int64
child 1, atomic_numbers: list<item: int64>
child 0, item: int64
child 2, symbols: list<item: string>
child 0, item: string
child 3, counts: struct<1: int64, 6: int64, 7: int64, 8: int64, 9: int64, 11: int64, 12: int64, 15: int64, 16: int64, (... 154 chars omitted)
child 0, 1: int64
child 1, 6: int64
child 2, 7: int64
child 3, 8: int64
child 4, 9: int64
child 5, 11: int64
child 6, 12: int64
child 7, 15: int64
child 8, 16: int64
child 9, 17: int64
child 10, 19: int64
child 11, 20: int64
child 12, 25: int64
child 13, 26: int64
child 14, 27: int64
child 15, 28: int64
child 16, 29: int64
child 17, 30: int64
child 18, 35: int64
child 19, 38: int64
child 20, 48: int64
child 21, 53: int64
child 22, 55: int64
label_y: struct<min: double, max: double, mean: double>
child 0, min: double
child 1, max: double
child 2, mean: double
n_total: int64
to
{'n_total': Value('int64'), 'sample_size': Value('int64'), 'atoms_per_structure': {'min': Value('int64'), 'max': Value('int64'), 'mean': Value('float64')}, 'bounding_box_extent_angstrom': {'side_min': Value('float64'), 'side_max': Value('float64'), 'diagonal_min': Value('float64'), 'diagonal_max': Value('float64')}, 'elements': {'count': Value('int64'), 'atomic_numbers': List(Value('int64')), 'symbols': List(Value('string')), 'counts': {'1': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '11': Value('int64'), '12': Value('int64'), '15': Value('int64'), '16': Value('int64'), '17': Value('int64'), '19': Value('int64'), '20': Value('int64'), '25': Value('int64'), '26': Value('int64'), '27': Value('int64'), '28': Value('int64'), '29': Value('int64'), '30': Value('int64'), '35': Value('int64'), '38': Value('int64'), '48': Value('int64'), '53': Value('int64'), '55': Value('int64')}}, 'label_y': {'min': Value('float64'), 'max': Value('float64'), 'mean': Value('float64')}, 'exact': {'num_complexes': Value('int64'), 'unique_pdb_ids': Value('int64'), 'components': {'protein': {'total_atoms': Value('int64'), 'min': Value('int64'), 'mean': Value('float64'), 'max': Value('int64'), 'zero_length': Value('int64')}, 'pocket': {'total_atoms': Value('int64'), 'min': Value('int64'), 'mean': Value('float64'), 'max': Value('int64'), 'zero_length': Value('int64')}, 'ligand': {'total_atoms': Value('int64'), 'min': Value('int64'), 'mean': Value('fl
...
Value('float64')}, 'splits': {'seq-id-30': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64'), 'total': Value('int64')}, 'seq-id-60': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64'), 'total': Value('int64')}}, 'ligand_bonds': {'total': Value('int64'), 'dtype': Value('string'), 'order_counts': {'1.0': Value('int64'), '2.0': Value('int64'), '3.0': Value('int64')}, 'convention': Value('string')}, 'file_sizes_gib': {'complexes.pt': Value('float64'), 'protein.pt': Value('float64')}, 'units': {'pos': Value('string'), 'bfactor': Value('string'), 'occupancy': Value('string'), 'neglog_aff': Value('string')}, 'sampled_fields': Value('string'), 'v1_regression': {'bitwise_identical': List(Value('string')), 'differs': List(Value('null')), 'ligand_edge_attr': {'v1_dtype': Value('string'), 'v2_dtype': Value('string'), 'values_equal': Value('bool')}, 'v1_merged_residue_complexes': {'protein': Value('int64'), 'pocket': Value('int64')}, 'v2_merged_residue_complexes': {'protein': Value('int64'), 'pocket': Value('int64')}, 'is_alpha_carbon': {'protein': {'v1_true': Value('int64'), 'v2_true': Value('int64'), 'lost': Value('int64'), 'gained': Value('int64'), 'z_of_lost': List(Value('int64')), 'lost_residues': Value('int64')}, 'pocket': {'v1_true': Value('int64'), 'v2_true': Value('int64'), 'lost': Value('int64'), 'gained': Value('int64'), 'z_of_lost': List(Value('int64')), 'lost_residues': Value('int64')}}, 'v1_missing_fields': List(Value('string'))}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
train: list<item: string>
child 0, item: string
val: list<item: string>
child 0, item: string
test: list<item: string>
child 0, item: string
bounding_box_extent_angstrom: struct<side_min: double, side_max: double, diagonal_min: double, diagonal_max: double>
child 0, side_min: double
child 1, side_max: double
child 2, diagonal_min: double
child 3, diagonal_max: double
atoms_per_structure: struct<min: int64, max: int64, mean: double>
child 0, min: int64
child 1, max: int64
child 2, mean: double
exact: struct<num_complexes: int64, unique_pdb_ids: int64, components: struct<protein: struct<total_atoms: (... 5760 chars omitted)
child 0, num_complexes: int64
child 1, unique_pdb_ids: int64
child 2, components: struct<protein: struct<total_atoms: int64, min: int64, mean: double, max: int64, zero_length: int64> (... 189 chars omitted)
child 0, protein: struct<total_atoms: int64, min: int64, mean: double, max: int64, zero_length: int64>
child 0, total_atoms: int64
child 1, min: int64
child 2, mean: double
child 3, max: int64
child 4, zero_length: int64
child 1, pocket: struct<total_atoms: int64, min: int64, mean: double, max: int64, zero_length: int64>
child 0, total_atoms: int64
child 1, min: int64
child 2, mean: double
child 3, max: int64
child 4, zero_length: int64
child 2, ligand: struct<total_atoms: int64, min: int64, mean: double, max: in
...
child 1, v2_true: int64
child 2, lost: int64
child 3, gained: int64
child 4, z_of_lost: list<item: int64>
child 0, item: int64
child 5, lost_residues: int64
child 6, v1_missing_fields: list<item: string>
child 0, item: string
sample_size: int64
elements: struct<count: int64, atomic_numbers: list<item: int64>, symbols: list<item: string>, counts: struct< (... 248 chars omitted)
child 0, count: int64
child 1, atomic_numbers: list<item: int64>
child 0, item: int64
child 2, symbols: list<item: string>
child 0, item: string
child 3, counts: struct<1: int64, 6: int64, 7: int64, 8: int64, 9: int64, 11: int64, 12: int64, 15: int64, 16: int64, (... 154 chars omitted)
child 0, 1: int64
child 1, 6: int64
child 2, 7: int64
child 3, 8: int64
child 4, 9: int64
child 5, 11: int64
child 6, 12: int64
child 7, 15: int64
child 8, 16: int64
child 9, 17: int64
child 10, 19: int64
child 11, 20: int64
child 12, 25: int64
child 13, 26: int64
child 14, 27: int64
child 15, 28: int64
child 16, 29: int64
child 17, 30: int64
child 18, 35: int64
child 19, 38: int64
child 20, 48: int64
child 21, 53: int64
child 22, 55: int64
label_y: struct<min: double, max: double, mean: double>
child 0, min: double
child 1, max: double
child 2, mean: double
n_total: int64
to
{'n_total': Value('int64'), 'sample_size': Value('int64'), 'atoms_per_structure': {'min': Value('int64'), 'max': Value('int64'), 'mean': Value('float64')}, 'bounding_box_extent_angstrom': {'side_min': Value('float64'), 'side_max': Value('float64'), 'diagonal_min': Value('float64'), 'diagonal_max': Value('float64')}, 'elements': {'count': Value('int64'), 'atomic_numbers': List(Value('int64')), 'symbols': List(Value('string')), 'counts': {'1': Value('int64'), '6': Value('int64'), '7': Value('int64'), '8': Value('int64'), '9': Value('int64'), '11': Value('int64'), '12': Value('int64'), '15': Value('int64'), '16': Value('int64'), '17': Value('int64'), '19': Value('int64'), '20': Value('int64'), '25': Value('int64'), '26': Value('int64'), '27': Value('int64'), '28': Value('int64'), '29': Value('int64'), '30': Value('int64'), '35': Value('int64'), '38': Value('int64'), '48': Value('int64'), '53': Value('int64'), '55': Value('int64')}}, 'label_y': {'min': Value('float64'), 'max': Value('float64'), 'mean': Value('float64')}, 'exact': {'num_complexes': Value('int64'), 'unique_pdb_ids': Value('int64'), 'components': {'protein': {'total_atoms': Value('int64'), 'min': Value('int64'), 'mean': Value('float64'), 'max': Value('int64'), 'zero_length': Value('int64')}, 'pocket': {'total_atoms': Value('int64'), 'min': Value('int64'), 'mean': Value('float64'), 'max': Value('int64'), 'zero_length': Value('int64')}, 'ligand': {'total_atoms': Value('int64'), 'min': Value('int64'), 'mean': Value('fl
...
Value('float64')}, 'splits': {'seq-id-30': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64'), 'total': Value('int64')}, 'seq-id-60': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64'), 'total': Value('int64')}}, 'ligand_bonds': {'total': Value('int64'), 'dtype': Value('string'), 'order_counts': {'1.0': Value('int64'), '2.0': Value('int64'), '3.0': Value('int64')}, 'convention': Value('string')}, 'file_sizes_gib': {'complexes.pt': Value('float64'), 'protein.pt': Value('float64')}, 'units': {'pos': Value('string'), 'bfactor': Value('string'), 'occupancy': Value('string'), 'neglog_aff': Value('string')}, 'sampled_fields': Value('string'), 'v1_regression': {'bitwise_identical': List(Value('string')), 'differs': List(Value('null')), 'ligand_edge_attr': {'v1_dtype': Value('string'), 'v2_dtype': Value('string'), 'values_equal': Value('bool')}, 'v1_merged_residue_complexes': {'protein': Value('int64'), 'pocket': Value('int64')}, 'v2_merged_residue_complexes': {'protein': Value('int64'), 'pocket': Value('int64')}, 'is_alpha_carbon': {'protein': {'v1_true': Value('int64'), 'v2_true': Value('int64'), 'lost': Value('int64'), 'gained': Value('int64'), 'z_of_lost': List(Value('int64')), 'lost_residues': Value('int64')}, 'pocket': {'v1_true': Value('int64'), 'v2_true': Value('int64'), 'lost': Value('int64'), 'gained': Value('int64'), 'z_of_lost': List(Value('int64')), 'lost_residues': Value('int64')}}, 'v1_missing_fields': List(Value('string'))}}}
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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