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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
event: string
next_epoch: int64
optimizer_step: int64
status: string
time_unix: double
metrics: struct<train/grad_norm: double, train/loss: double, train/lr: double, train/steps_per_second: double (... 5631 chars omitted)
child 0, train/grad_norm: double
child 1, train/loss: double
child 2, train/lr: double
child 3, train/steps_per_second: double
child 4, val/mae_recon: double
child 5, hear/beijing_opera/knn/k1/mAP: double
child 6, hear/beijing_opera/knn/k1/top1_acc: double
child 7, hear/beijing_opera/knn/k10/mAP: double
child 8, hear/beijing_opera/knn/k10/top1_acc: double
child 9, hear/beijing_opera/knn/k5/mAP: double
child 10, hear/beijing_opera/knn/k5/top1_acc: double
child 11, hear/beijing_opera/linear/linear/mAP: double
child 12, hear/beijing_opera/linear/linear/probe_accuracy: double
child 13, hear/beijing_opera/linear/linear/top1_acc: double
child 14, hear/dcase2016_task2/event_mlp/event_onset_200ms_fms: double
child 15, hear/esc50/knn/k1/mAP: double
child 16, hear/esc50/knn/k1/top1_acc: double
child 17, hear/esc50/knn/k10/mAP: double
child 18, hear/esc50/knn/k10/top1_acc: double
child 19, hear/esc50/knn/k5/mAP: double
child 20, hear/esc50/knn/k5/top1_acc: double
child 21, hear/esc50/linear/linear/mAP: double
child 22, hear/esc50/linear/linear/probe_accuracy: double
child 23, hear/esc50/linear/linear/top1_acc: double
child 24, hear/gunshot_triangulation/knn/k1/mAP: double
child 25, hear/gunshot_triangulation/knn/k1/to
...
t64
provenance: struct<policy: string, swarm: string, swarm_size: int64, surrogate: string, repetition_factor: doubl (... 26 chars omitted)
child 0, policy: string
child 1, swarm: string
child 2, swarm_size: int64
child 3, surrogate: string
child 4, repetition_factor: double
child 5, requested_clips: int64
rounding: string
schema_version: int64
deduplicated_count: int64
minimum_weight: double
sample_multiplier: int64
cluster_source: string
sampling_prior: list<item: double>
child 0, item: double
max_concentration: double
num_concentrations: int64
min_concentration: double
proxy_budget: int64
seed: int64
concentration_grid: list<item: double>
child 0, item: double
temperature: double
minimum_examples: int64
cluster_source_revision: string
num_clusters: int64
proposal_count: int64
paper: string
max_usage: double
upper_bounds: list<item: double>
child 0, item: double
distributions: list<item: struct<dist_id: int64, kind: string, name: string, note: string, weights: list<item: doub (... 5 chars omitted)
child 0, item: struct<dist_id: int64, kind: string, name: string, note: string, weights: list<item: double>>
child 0, dist_id: int64
child 1, kind: string
child 2, name: string
child 3, note: string
child 4, weights: list<item: double>
child 0, item: double
method: string
natural_proportions: list<item: double>
child 0, item: double
reference_implementation: string
adaptations: list<item: string>
child 0, item: string
to
{'accepted_count': Value('int64'), 'adaptations': List(Value('string')), 'cluster_source': Value('string'), 'cluster_source_revision': Value('string'), 'cluster_stats_schema_version': Value('int64'), 'concentration_grid': List(Value('float64')), 'deduplicated_count': Value('int64'), 'max_concentration': Value('float64'), 'max_usage': Value('float64'), 'method': Value('string'), 'min_concentration': Value('float64'), 'minimum_examples': Value('int64'), 'minimum_weight': Value('float64'), 'natural_proportions': List(Value('float64')), 'num_clusters': Value('int64'), 'num_concentrations': Value('int64'), 'paper': Value('string'), 'proposal_count': Value('int64'), 'proxy_budget': Value('int64'), 'reference_implementation': Value('string'), 'rounding': Value('string'), 'sample_multiplier': Value('int64'), 'sampling_prior': List(Value('float64')), 'schema_version': Value('int64'), 'seed': Value('int64'), 'temperature': Value('float64'), 'upper_bounds': List(Value('float64')), 'distributions': List({'dist_id': Value('int64'), 'kind': Value('string'), 'name': Value('string'), 'note': Value('string'), 'weights': List(Value('float64'))}), 'num_distributions': Value('int64'), 'provenance': {'policy': Value('string'), 'swarm': Value('string'), 'swarm_size': Value('int64'), 'surrogate': Value('string'), 'repetition_factor': Value('float64'), 'requested_clips': Value('int64')}}
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
event: string
next_epoch: int64
optimizer_step: int64
status: string
time_unix: double
metrics: struct<train/grad_norm: double, train/loss: double, train/lr: double, train/steps_per_second: double (... 5631 chars omitted)
child 0, train/grad_norm: double
child 1, train/loss: double
child 2, train/lr: double
child 3, train/steps_per_second: double
child 4, val/mae_recon: double
child 5, hear/beijing_opera/knn/k1/mAP: double
child 6, hear/beijing_opera/knn/k1/top1_acc: double
child 7, hear/beijing_opera/knn/k10/mAP: double
child 8, hear/beijing_opera/knn/k10/top1_acc: double
child 9, hear/beijing_opera/knn/k5/mAP: double
child 10, hear/beijing_opera/knn/k5/top1_acc: double
child 11, hear/beijing_opera/linear/linear/mAP: double
child 12, hear/beijing_opera/linear/linear/probe_accuracy: double
child 13, hear/beijing_opera/linear/linear/top1_acc: double
child 14, hear/dcase2016_task2/event_mlp/event_onset_200ms_fms: double
child 15, hear/esc50/knn/k1/mAP: double
child 16, hear/esc50/knn/k1/top1_acc: double
child 17, hear/esc50/knn/k10/mAP: double
child 18, hear/esc50/knn/k10/top1_acc: double
child 19, hear/esc50/knn/k5/mAP: double
child 20, hear/esc50/knn/k5/top1_acc: double
child 21, hear/esc50/linear/linear/mAP: double
child 22, hear/esc50/linear/linear/probe_accuracy: double
child 23, hear/esc50/linear/linear/top1_acc: double
child 24, hear/gunshot_triangulation/knn/k1/mAP: double
child 25, hear/gunshot_triangulation/knn/k1/to
...
t64
provenance: struct<policy: string, swarm: string, swarm_size: int64, surrogate: string, repetition_factor: doubl (... 26 chars omitted)
child 0, policy: string
child 1, swarm: string
child 2, swarm_size: int64
child 3, surrogate: string
child 4, repetition_factor: double
child 5, requested_clips: int64
rounding: string
schema_version: int64
deduplicated_count: int64
minimum_weight: double
sample_multiplier: int64
cluster_source: string
sampling_prior: list<item: double>
child 0, item: double
max_concentration: double
num_concentrations: int64
min_concentration: double
proxy_budget: int64
seed: int64
concentration_grid: list<item: double>
child 0, item: double
temperature: double
minimum_examples: int64
cluster_source_revision: string
num_clusters: int64
proposal_count: int64
paper: string
max_usage: double
upper_bounds: list<item: double>
child 0, item: double
distributions: list<item: struct<dist_id: int64, kind: string, name: string, note: string, weights: list<item: doub (... 5 chars omitted)
child 0, item: struct<dist_id: int64, kind: string, name: string, note: string, weights: list<item: double>>
child 0, dist_id: int64
child 1, kind: string
child 2, name: string
child 3, note: string
child 4, weights: list<item: double>
child 0, item: double
method: string
natural_proportions: list<item: double>
child 0, item: double
reference_implementation: string
adaptations: list<item: string>
child 0, item: string
to
{'accepted_count': Value('int64'), 'adaptations': List(Value('string')), 'cluster_source': Value('string'), 'cluster_source_revision': Value('string'), 'cluster_stats_schema_version': Value('int64'), 'concentration_grid': List(Value('float64')), 'deduplicated_count': Value('int64'), 'max_concentration': Value('float64'), 'max_usage': Value('float64'), 'method': Value('string'), 'min_concentration': Value('float64'), 'minimum_examples': Value('int64'), 'minimum_weight': Value('float64'), 'natural_proportions': List(Value('float64')), 'num_clusters': Value('int64'), 'num_concentrations': Value('int64'), 'paper': Value('string'), 'proposal_count': Value('int64'), 'proxy_budget': Value('int64'), 'reference_implementation': Value('string'), 'rounding': Value('string'), 'sample_multiplier': Value('int64'), 'sampling_prior': List(Value('float64')), 'schema_version': Value('int64'), 'seed': Value('int64'), 'temperature': Value('float64'), 'upper_bounds': List(Value('float64')), 'distributions': List({'dist_id': Value('int64'), 'kind': Value('string'), 'name': Value('string'), 'note': Value('string'), 'weights': List(Value('float64'))}), 'num_distributions': Value('int64'), 'provenance': {'policy': Value('string'), 'swarm': Value('string'), 'swarm_size': Value('int64'), 'surrogate': Value('string'), 'repetition_factor': Value('float64'), 'requested_clips': Value('int64')}}
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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