Dataset Viewer
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
model_id: string
config: string
layers: string
capture: string
expansion: int64
pool_batches: int64
pool_forward_fusion: int64
max_steps: int64
target_l0: int64
hf_repo_type: string
results: struct<layer_00: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: (... 8642 chars omitted)
child 0, layer_00: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: double, nonlinea (... 224 chars omitted)
child 0, recon_loss: double
child 1, mean_l0: double
child 2, dead_pct: double
child 3, resampled: int64
child 4, ev: double
child 5, nonlinear_err: null
child 6, linear_err: null
child 7, qo_gap: double
child 8, activation_norm_probe: double
child 9, activation_norm_ref: double
child 10, initial_l0_probe: double
child 11, initial_lr_multiplier: double
child 12, early_pulse_multiplier: double
child 13, layer_lr_multiplier: double
child 1, layer_01: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: double, nonlinea (... 224 chars omitted)
child 0, recon_loss: double
child 1, mean_l0: double
child 2, dead_pct: double
child 3, resampled: int64
child 4, ev: double
child 5, nonlinear_err: null
child 6, linear_err: null
child 7, qo_gap: double
child 8, activation_norm_probe: double
child 9, activation_norm_ref: double
child 10, initial_l0_probe:
...
64>
child 0, item: int64
child 10, exclude_special: bool
child 11, masked_fraction_observed: double
child 12, tokens_seen_nominal: int64
child 13, tokens_seen: int64
child 14, stop_reason: string
child 15, stop_step: int64
child 16, early_stopped: bool
child 17, final_ev: double
child 18, final_l0: double
child 19, final_dead_pct: double
child 20, wandb_project: string
child 21, wandb_run: string
child 22, written_utc: timestamp[s]
early_stopped: bool
scheduler_phase_summary: struct<phase: string, phase_step: int64, pin_entry_step: int64, pinned_lambda: double, pin_ev_count: (... 31 chars omitted)
child 0, phase: string
child 1, phase_step: int64
child 2, pin_entry_step: int64
child 3, pinned_lambda: double
child 4, pin_ev_count: int64
child 5, pin_retry_count: int64
scheduler_phase: string
d_in: int64
final_metrics: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: double, nonlinea (... 224 chars omitted)
child 0, recon_loss: double
child 1, mean_l0: double
child 2, dead_pct: double
child 3, resampled: int64
child 4, ev: double
child 5, nonlinear_err: null
child 6, linear_err: null
child 7, qo_gap: double
child 8, activation_norm_probe: double
child 9, activation_norm_ref: double
child 10, initial_l0_probe: double
child 11, initial_lr_multiplier: double
child 12, early_pulse_multiplier: double
child 13, layer_lr_multiplier: double
scheduler_transitions: int64
seed: int64
to
{'layer': Value('int64'), 'seed': Value('int64'), 'model_id': Value('string'), 'd_in': Value('int64'), 'n_features': Value('int64'), 'k': Value('int64'), 'batch_tokens': Value('int64'), 'n_steps': Value('int64'), 'lr': Value('float64'), 'lambda_l0': Value('float64'), 'tier': Value('string'), 'preflight': {'activation_norm_probe': Value('float64'), 'activation_norm_ref': Value('float64'), 'initial_l0_probe': Value('float64'), 'initial_lr_multiplier': Value('float64'), 'early_pulse_multiplier': Value('float64'), 'layer_lr_multiplier': Value('float64')}, 'scheduler_mode': Value('string'), 'scheduler_phase': Value('string'), 'scheduler_phase_summary': {'phase': Value('string'), 'phase_step': Value('int64'), 'pin_entry_step': Value('int64'), 'pinned_lambda': Value('float64'), 'pin_ev_count': Value('int64'), 'pin_retry_count': Value('int64')}, 'scheduler_transitions': Value('int64'), 'total_tokens': Value('int64'), 'early_stopped': Value('bool'), 'path': Value('string'), 'best_ev': Value('float64'), 'best_ev_step': Value('int64'), 'final_metrics': {'recon_loss': Value('float64'), 'mean_l0': Value('float64'), 'dead_pct': Value('float64'), 'resampled': Value('int64'), 'ev': Value('float64'), 'nonlinear_err': Value('null'), 'linear_err': Value('null'), 'qo_gap': Value('float64'), 'activation_norm_probe': Value('float64'), 'activation_norm_ref': Value('float64'), 'initial_l0_probe': Value('float64'), 'initial_lr_multiplier': Value('float64'), 'early_pulse_multiplier': Value('float64'), 'layer_lr_multiplier': Value('float64')}, 'training_curve': {'recon_loss': List(Value('float64')), 'mean_l0': List(Value('float64')), 'dead_pct': List(Value('float64')), 'resampled': List(Value('int64')), 'ev': List(Value('float64')), 'nonlinear_err': List(Value('null')), 'linear_err': List(Value('null')), 'qo_gap': List(Value('float64'))}, 'provenance': {'job_id': Value('string'), 'trainer_commit': Value('string'), 'patch_sha256': Value('string'), 'image_ref': Value('string'), 'corpus': Value('string'), 'corpus_commit': Value('string'), 'seq_len': Value('int64'), 'pool_batches': Value('int64'), 'pool_backend': Value('string'), 'exclude_ids': List(Value('int64')), 'exclude_special': Value('bool'), 'masked_fraction_observed': Value('float64'), 'tokens_seen_nominal': Value('int64'), 'tokens_seen': Value('int64'), 'stop_reason': Value('string'), 'stop_step': Value('int64'), 'early_stopped': Value('bool'), 'final_ev': Value('float64'), 'final_l0': Value('float64'), 'final_dead_pct': Value('float64'), 'wandb_project': Value('string'), 'wandb_run': Value('string'), 'written_utc': Value('timestamp[s]')}}
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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 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_id: string
config: string
layers: string
capture: string
expansion: int64
pool_batches: int64
pool_forward_fusion: int64
max_steps: int64
target_l0: int64
hf_repo_type: string
results: struct<layer_00: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: (... 8642 chars omitted)
child 0, layer_00: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: double, nonlinea (... 224 chars omitted)
child 0, recon_loss: double
child 1, mean_l0: double
child 2, dead_pct: double
child 3, resampled: int64
child 4, ev: double
child 5, nonlinear_err: null
child 6, linear_err: null
child 7, qo_gap: double
child 8, activation_norm_probe: double
child 9, activation_norm_ref: double
child 10, initial_l0_probe: double
child 11, initial_lr_multiplier: double
child 12, early_pulse_multiplier: double
child 13, layer_lr_multiplier: double
child 1, layer_01: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: double, nonlinea (... 224 chars omitted)
child 0, recon_loss: double
child 1, mean_l0: double
child 2, dead_pct: double
child 3, resampled: int64
child 4, ev: double
child 5, nonlinear_err: null
child 6, linear_err: null
child 7, qo_gap: double
child 8, activation_norm_probe: double
child 9, activation_norm_ref: double
child 10, initial_l0_probe:
...
64>
child 0, item: int64
child 10, exclude_special: bool
child 11, masked_fraction_observed: double
child 12, tokens_seen_nominal: int64
child 13, tokens_seen: int64
child 14, stop_reason: string
child 15, stop_step: int64
child 16, early_stopped: bool
child 17, final_ev: double
child 18, final_l0: double
child 19, final_dead_pct: double
child 20, wandb_project: string
child 21, wandb_run: string
child 22, written_utc: timestamp[s]
early_stopped: bool
scheduler_phase_summary: struct<phase: string, phase_step: int64, pin_entry_step: int64, pinned_lambda: double, pin_ev_count: (... 31 chars omitted)
child 0, phase: string
child 1, phase_step: int64
child 2, pin_entry_step: int64
child 3, pinned_lambda: double
child 4, pin_ev_count: int64
child 5, pin_retry_count: int64
scheduler_phase: string
d_in: int64
final_metrics: struct<recon_loss: double, mean_l0: double, dead_pct: double, resampled: int64, ev: double, nonlinea (... 224 chars omitted)
child 0, recon_loss: double
child 1, mean_l0: double
child 2, dead_pct: double
child 3, resampled: int64
child 4, ev: double
child 5, nonlinear_err: null
child 6, linear_err: null
child 7, qo_gap: double
child 8, activation_norm_probe: double
child 9, activation_norm_ref: double
child 10, initial_l0_probe: double
child 11, initial_lr_multiplier: double
child 12, early_pulse_multiplier: double
child 13, layer_lr_multiplier: double
scheduler_transitions: int64
seed: int64
to
{'layer': Value('int64'), 'seed': Value('int64'), 'model_id': Value('string'), 'd_in': Value('int64'), 'n_features': Value('int64'), 'k': Value('int64'), 'batch_tokens': Value('int64'), 'n_steps': Value('int64'), 'lr': Value('float64'), 'lambda_l0': Value('float64'), 'tier': Value('string'), 'preflight': {'activation_norm_probe': Value('float64'), 'activation_norm_ref': Value('float64'), 'initial_l0_probe': Value('float64'), 'initial_lr_multiplier': Value('float64'), 'early_pulse_multiplier': Value('float64'), 'layer_lr_multiplier': Value('float64')}, 'scheduler_mode': Value('string'), 'scheduler_phase': Value('string'), 'scheduler_phase_summary': {'phase': Value('string'), 'phase_step': Value('int64'), 'pin_entry_step': Value('int64'), 'pinned_lambda': Value('float64'), 'pin_ev_count': Value('int64'), 'pin_retry_count': Value('int64')}, 'scheduler_transitions': Value('int64'), 'total_tokens': Value('int64'), 'early_stopped': Value('bool'), 'path': Value('string'), 'best_ev': Value('float64'), 'best_ev_step': Value('int64'), 'final_metrics': {'recon_loss': Value('float64'), 'mean_l0': Value('float64'), 'dead_pct': Value('float64'), 'resampled': Value('int64'), 'ev': Value('float64'), 'nonlinear_err': Value('null'), 'linear_err': Value('null'), 'qo_gap': Value('float64'), 'activation_norm_probe': Value('float64'), 'activation_norm_ref': Value('float64'), 'initial_l0_probe': Value('float64'), 'initial_lr_multiplier': Value('float64'), 'early_pulse_multiplier': Value('float64'), 'layer_lr_multiplier': Value('float64')}, 'training_curve': {'recon_loss': List(Value('float64')), 'mean_l0': List(Value('float64')), 'dead_pct': List(Value('float64')), 'resampled': List(Value('int64')), 'ev': List(Value('float64')), 'nonlinear_err': List(Value('null')), 'linear_err': List(Value('null')), 'qo_gap': List(Value('float64'))}, 'provenance': {'job_id': Value('string'), 'trainer_commit': Value('string'), 'patch_sha256': Value('string'), 'image_ref': Value('string'), 'corpus': Value('string'), 'corpus_commit': Value('string'), 'seq_len': Value('int64'), 'pool_batches': Value('int64'), 'pool_backend': Value('string'), 'exclude_ids': List(Value('int64')), 'exclude_special': Value('bool'), 'masked_fraction_observed': Value('float64'), 'tokens_seen_nominal': Value('int64'), 'tokens_seen': Value('int64'), 'stop_reason': Value('string'), 'stop_step': Value('int64'), 'early_stopped': Value('bool'), 'final_ev': Value('float64'), 'final_l0': Value('float64'), 'final_dead_pct': Value('float64'), 'wandb_project': Value('string'), 'wandb_run': Value('string'), 'written_utc': Value('timestamp[s]')}}
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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