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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
config: struct<epochs: int64, frames: int64, grid: int64, repo_id: string, rollout_model_steps: int64, save_ (... 152 chars omitted)
child 0, epochs: int64
child 1, frames: int64
child 2, grid: int64
child 3, repo_id: string
child 4, rollout_model_steps: int64
child 5, save_every: int64
child 6, seed_base: int64
child 7, stat_seeds: int64
child 8, stats_seed_base: int64
child 9, test_trajectories: int64
child 10, train_trajectories: int64
child 11, val_trajectories: int64
dataset: struct<base_dir: string, frames: int64, grid: int64, save_every_base_steps: int64, supervised_ndt_fr (... 88 chars omitted)
child 0, base_dir: string
child 1, frames: int64
child 2, grid: int64
child 3, save_every_base_steps: int64
child 4, supervised_ndt_frames: int64
child 5, supervised_stride_base_steps: int64
child 6, test: int64
child 7, train: int64
child 8, val: int64
job: struct<flavor: null, id: string>
child 0, flavor: null
child 1, id: string
official_commit: string
official_repo: string
training: struct<checkpoint: string, elapsed_seconds: double, result: struct<evaluation: struct<rollout: struc (... 454 chars omitted)
child 0, checkpoint: string
child 1, elapsed_seconds: double
child 2, result: struct<evaluation: struct<rollout: struct<cons_drift_max: double, cons_drift_mean: double, mae: doub (... 158 chars omitted)
child 0, evaluation: struct<rollout: struct<cons_drift_max: double, cons_drift_mean: double, mae: double, mae_std:
...
child 1, cons_drift_mean: double
child 2, mae: double
child 3, mae_std: double
child 4, rmse: double
child 5, viol_lower: double
child 6, viol_upper: double
child 1, experiment_name: string
child 2, training: struct<best_loss: double, total_time: double>
child 0, best_loss: double
child 1, total_time: double
child 3, rollout_detail: struct<cons_drift_at_T1.0: double, cons_drift_at_T1.0_std: double, mae_at_T1.0: double, mae_at_T1.0_ (... 342 chars omitted)
child 0, cons_drift_at_T1.0: double
child 1, cons_drift_at_T1.0_std: double
child 2, mae_at_T1.0: double
child 3, mae_at_T1.0_std: double
child 4, max_steps: double
child 5, max_value_overall: double
child 6, min_value_overall: double
child 7, num_trajectories: double
child 8, rmse_at_T1.0: double
child 9, sw_cons_drift_h_mean: double
child 10, sw_cons_drift_h_std: double
child 11, sw_h_cond_mag_mean: double
child 12, sw_h_viol_rate_mean: double
child 13, sw_mae_h_mean: double
child 14, sw_mae_h_std: double
child 15, sw_mae_mx_mean: double
child 16, sw_mae_my_mean: double
child 4, seed: int64
to
{'config': {'epochs': Value('int64'), 'repo_id': Value('string'), 'scale': Value('string'), 'seeds': Value('string')}, 'dataset': {'base_dir': Value('string'), 'category_config': {'CaseA1': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'CaseA2': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'CaseB1': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'CaseB2': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}}, 'fixed_dt': Value('float64'), 'saved_grid': List(Value('int64')), 'space_downsample': Value('int64'), 't_final': Value('float64'), 'test': Value('int64'), 'time_downsample': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'job': {'flavor': Value('null'), 'id': Value('string')}, 'official_commit': Value('string'), 'official_repo': Value('string'), 'results': {'aggregate': {'CNN_SW_Proj_box_mass_pf': {'h_cons_mean': Value('float64'), 'h_mae_mean': Value('float64'), 'h_mae_std_across_seeds': Value('float64'), 'h_viol_mean': Value('float64'), 'min_value_worst': Value('float64'), 'n_seeds': Value('int64'), 'n_test_trajectories_per_seed': Value('float64'), 'overall_mae_T1_mean': Value('float64')}, 'FluxNet_SW_LAP_pf': {'h_cons_mean': Value('float64'), 'h_mae_mean': Value('float64'), 'h_mae_std_across_seeds': Value('float64'), 'h_viol_mean': Value('float64'), '
...
: Value('float64'), 'sw_cons_drift_h_mean': Value('float64'), 'sw_cons_drift_h_std': Value('float64'), 'sw_h_cond_mag_mean': Value('float64'), 'sw_h_viol_rate_mean': Value('float64'), 'sw_mae_h_mean': Value('float64'), 'sw_mae_h_std': Value('float64'), 'sw_mae_mx_mean': Value('float64'), 'sw_mae_my_mean': Value('float64')}, 'seed': Value('int64')}, 'FluxNet_SW_LAP_pf_seed42': {'elapsed_seconds': Value('float64'), 'model': Value('string'), 'result': {'evaluation': {'rollout': {'cons_drift_max': Value('float64'), 'cons_drift_mean': Value('float64'), 'mae': Value('float64'), 'mae_std': Value('float64'), 'rmse': Value('float64'), 'viol_lower': Value('float64'), 'viol_upper': Value('float64')}}, 'experiment_name': Value('string'), 'training': {'best_loss': Value('float64'), 'total_time': Value('float64')}}, 'rollout_detail': {'cons_drift_at_T1.0': Value('float64'), 'cons_drift_at_T1.0_std': Value('float64'), 'mae_at_T1.0': Value('float64'), 'mae_at_T1.0_std': Value('float64'), 'max_steps': Value('float64'), 'max_value_overall': Value('float64'), 'min_value_overall': Value('float64'), 'num_trajectories': Value('float64'), 'rmse_at_T1.0': Value('float64'), 'sw_cons_drift_h_mean': Value('float64'), 'sw_cons_drift_h_std': Value('float64'), 'sw_h_cond_mag_mean': Value('float64'), 'sw_h_viol_rate_mean': Value('float64'), 'sw_mae_h_mean': Value('float64'), 'sw_mae_h_std': Value('float64'), 'sw_mae_mx_mean': Value('float64'), 'sw_mae_my_mean': Value('float64')}, 'seed': 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
config: struct<epochs: int64, frames: int64, grid: int64, repo_id: string, rollout_model_steps: int64, save_ (... 152 chars omitted)
child 0, epochs: int64
child 1, frames: int64
child 2, grid: int64
child 3, repo_id: string
child 4, rollout_model_steps: int64
child 5, save_every: int64
child 6, seed_base: int64
child 7, stat_seeds: int64
child 8, stats_seed_base: int64
child 9, test_trajectories: int64
child 10, train_trajectories: int64
child 11, val_trajectories: int64
dataset: struct<base_dir: string, frames: int64, grid: int64, save_every_base_steps: int64, supervised_ndt_fr (... 88 chars omitted)
child 0, base_dir: string
child 1, frames: int64
child 2, grid: int64
child 3, save_every_base_steps: int64
child 4, supervised_ndt_frames: int64
child 5, supervised_stride_base_steps: int64
child 6, test: int64
child 7, train: int64
child 8, val: int64
job: struct<flavor: null, id: string>
child 0, flavor: null
child 1, id: string
official_commit: string
official_repo: string
training: struct<checkpoint: string, elapsed_seconds: double, result: struct<evaluation: struct<rollout: struc (... 454 chars omitted)
child 0, checkpoint: string
child 1, elapsed_seconds: double
child 2, result: struct<evaluation: struct<rollout: struct<cons_drift_max: double, cons_drift_mean: double, mae: doub (... 158 chars omitted)
child 0, evaluation: struct<rollout: struct<cons_drift_max: double, cons_drift_mean: double, mae: double, mae_std:
...
child 1, cons_drift_mean: double
child 2, mae: double
child 3, mae_std: double
child 4, rmse: double
child 5, viol_lower: double
child 6, viol_upper: double
child 1, experiment_name: string
child 2, training: struct<best_loss: double, total_time: double>
child 0, best_loss: double
child 1, total_time: double
child 3, rollout_detail: struct<cons_drift_at_T1.0: double, cons_drift_at_T1.0_std: double, mae_at_T1.0: double, mae_at_T1.0_ (... 342 chars omitted)
child 0, cons_drift_at_T1.0: double
child 1, cons_drift_at_T1.0_std: double
child 2, mae_at_T1.0: double
child 3, mae_at_T1.0_std: double
child 4, max_steps: double
child 5, max_value_overall: double
child 6, min_value_overall: double
child 7, num_trajectories: double
child 8, rmse_at_T1.0: double
child 9, sw_cons_drift_h_mean: double
child 10, sw_cons_drift_h_std: double
child 11, sw_h_cond_mag_mean: double
child 12, sw_h_viol_rate_mean: double
child 13, sw_mae_h_mean: double
child 14, sw_mae_h_std: double
child 15, sw_mae_mx_mean: double
child 16, sw_mae_my_mean: double
child 4, seed: int64
to
{'config': {'epochs': Value('int64'), 'repo_id': Value('string'), 'scale': Value('string'), 'seeds': Value('string')}, 'dataset': {'base_dir': Value('string'), 'category_config': {'CaseA1': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'CaseA2': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'CaseB1': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'CaseB2': {'test': Value('int64'), 'total': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}}, 'fixed_dt': Value('float64'), 'saved_grid': List(Value('int64')), 'space_downsample': Value('int64'), 't_final': Value('float64'), 'test': Value('int64'), 'time_downsample': Value('int64'), 'train': Value('int64'), 'val': Value('int64')}, 'job': {'flavor': Value('null'), 'id': Value('string')}, 'official_commit': Value('string'), 'official_repo': Value('string'), 'results': {'aggregate': {'CNN_SW_Proj_box_mass_pf': {'h_cons_mean': Value('float64'), 'h_mae_mean': Value('float64'), 'h_mae_std_across_seeds': Value('float64'), 'h_viol_mean': Value('float64'), 'min_value_worst': Value('float64'), 'n_seeds': Value('int64'), 'n_test_trajectories_per_seed': Value('float64'), 'overall_mae_T1_mean': Value('float64')}, 'FluxNet_SW_LAP_pf': {'h_cons_mean': Value('float64'), 'h_mae_mean': Value('float64'), 'h_mae_std_across_seeds': Value('float64'), 'h_viol_mean': Value('float64'), '
...
: Value('float64'), 'sw_cons_drift_h_mean': Value('float64'), 'sw_cons_drift_h_std': Value('float64'), 'sw_h_cond_mag_mean': Value('float64'), 'sw_h_viol_rate_mean': Value('float64'), 'sw_mae_h_mean': Value('float64'), 'sw_mae_h_std': Value('float64'), 'sw_mae_mx_mean': Value('float64'), 'sw_mae_my_mean': Value('float64')}, 'seed': Value('int64')}, 'FluxNet_SW_LAP_pf_seed42': {'elapsed_seconds': Value('float64'), 'model': Value('string'), 'result': {'evaluation': {'rollout': {'cons_drift_max': Value('float64'), 'cons_drift_mean': Value('float64'), 'mae': Value('float64'), 'mae_std': Value('float64'), 'rmse': Value('float64'), 'viol_lower': Value('float64'), 'viol_upper': Value('float64')}}, 'experiment_name': Value('string'), 'training': {'best_loss': Value('float64'), 'total_time': Value('float64')}}, 'rollout_detail': {'cons_drift_at_T1.0': Value('float64'), 'cons_drift_at_T1.0_std': Value('float64'), 'mae_at_T1.0': Value('float64'), 'mae_at_T1.0_std': Value('float64'), 'max_steps': Value('float64'), 'max_value_overall': Value('float64'), 'min_value_overall': Value('float64'), 'num_trajectories': Value('float64'), 'rmse_at_T1.0': Value('float64'), 'sw_cons_drift_h_mean': Value('float64'), 'sw_cons_drift_h_std': Value('float64'), 'sw_h_cond_mag_mean': Value('float64'), 'sw_h_viol_rate_mean': Value('float64'), 'sw_mae_h_mean': Value('float64'), 'sw_mae_h_std': Value('float64'), 'sw_mae_mx_mean': Value('float64'), 'sw_mae_my_mean': Value('float64')}, 'seed': 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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