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: TypeError
Message: Couldn't cast array of type
struct<type: string, n_obs_steps: int64, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.cam_high: struct<type: string, shape: list<item: int64>>, observation.images.cam_left_wrist: struct<type: string, shape: list<item: int64>>, observation.images.cam_right_wrist: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, use_peft: bool, push_to_hub: bool, repo_id: null, private: null, tags: null, license: null, pretrained_path: string, chunk_size: int64, n_action_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, max_state_dim: int64, max_action_dim: int64, resize_imgs_with_padding: list<item: int64>, empty_cameras: int64, adapt_to_pi_aloha: bool, use_delta_joint_actions_aloha: bool, tokenizer_max_length: int64, num_steps: int64, use_cache: bool, freeze_vision_encoder: bool, train_expert_only: bool, train_state_proj: bool, optimizer_lr: double, optimizer_betas: list<item: double>, optimizer_eps: double, optimizer_weight_decay: double, optimizer_grad_clip_norm: double, scheduler_warmup_steps: int64, scheduler_decay_steps: int64, scheduler_decay_lr: double, vlm_model_name: string, load_vlm_weights: bool, add_image_special_tokens: bool, attention_mode: string, prefix_length: int64, pad_language_to: string, num_expert_layers: int64, num_vlm_layers: int64, self_attn_every_n_layers: int64, expert_width_multiplier: double, min_period: double, max_period: double, rtc_config: null, compile_model: bool, compile_mode: string>
to
{'type': Value('string'), 'n_obs_steps': Value('int64'), 'input_features': {'observation.state': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.cam_high': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.cam_left_wrist': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.cam_right_wrist': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'output_features': {'action': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'device': Value('string'), 'use_amp': Value('bool'), 'use_peft': Value('bool'), 'push_to_hub': Value('bool'), 'repo_id': Value('null'), 'private': Value('null'), 'tags': Value('null'), 'license': Value('null'), 'pretrained_path': Value('null'), 'horizon': Value('int64'), 'n_action_steps': Value('int64'), 'normalization_mapping': {'VISUAL': Value('string'), 'STATE': Value('string'), 'ACTION': Value('string')}, 'drop_n_last_frames': Value('int64'), 'vision_backbone': Value('string'), 'resize_shape': Value('null'), 'crop_ratio': Value('float64'), 'crop_shape': Value('null'), 'crop_is_random': Value('bool'), 'pretrained_backbone_weights': Value('null'), 'use_group_norm': Value('bool'), 'spatial_softmax_num_keypoints': Value('int64'), 'use_separate_rgb_encoder_per_camera': Value('bool'), 'down_dims': List(Value('int64')), 'kernel_size': Value('int64'), 'n_groups': Value('int64'), 'diffusion_step_embed_dim': Value('int64'), 'use_film_scale_modulation': Value('bool'), 'noise_scheduler_type': Value('string'), 'num_train_timesteps': Value('int64'), 'beta_schedule': Value('string'), 'beta_start': Value('float64'), 'beta_end': Value('float64'), 'prediction_type': Value('string'), 'clip_sample': Value('bool'), 'clip_sample_range': Value('float64'), 'num_inference_steps': Value('int64'), 'compile_model': Value('bool'), 'compile_mode': Value('string'), 'do_mask_loss_for_padding': Value('bool'), 'optimizer_lr': Value('float64'), 'optimizer_betas': List(Value('float64')), 'optimizer_eps': Value('float64'), 'optimizer_weight_decay': Value('float64'), 'scheduler_name': Value('string'), 'scheduler_warmup_steps': Value('int64')}
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 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<type: string, n_obs_steps: int64, input_features: struct<observation.state: struct<type: string, shape: list<item: int64>>, observation.images.cam_high: struct<type: string, shape: list<item: int64>>, observation.images.cam_left_wrist: struct<type: string, shape: list<item: int64>>, observation.images.cam_right_wrist: struct<type: string, shape: list<item: int64>>>, output_features: struct<action: struct<type: string, shape: list<item: int64>>>, device: string, use_amp: bool, use_peft: bool, push_to_hub: bool, repo_id: null, private: null, tags: null, license: null, pretrained_path: string, chunk_size: int64, n_action_steps: int64, normalization_mapping: struct<VISUAL: string, STATE: string, ACTION: string>, max_state_dim: int64, max_action_dim: int64, resize_imgs_with_padding: list<item: int64>, empty_cameras: int64, adapt_to_pi_aloha: bool, use_delta_joint_actions_aloha: bool, tokenizer_max_length: int64, num_steps: int64, use_cache: bool, freeze_vision_encoder: bool, train_expert_only: bool, train_state_proj: bool, optimizer_lr: double, optimizer_betas: list<item: double>, optimizer_eps: double, optimizer_weight_decay: double, optimizer_grad_clip_norm: double, scheduler_warmup_steps: int64, scheduler_decay_steps: int64, scheduler_decay_lr: double, vlm_model_name: string, load_vlm_weights: bool, add_image_special_tokens: bool, attention_mode: string, prefix_length: int64, pad_language_to: string, num_expert_layers: int64, num_vlm_layers: int64, self_attn_every_n_layers: int64, expert_width_multiplier: double, min_period: double, max_period: double, rtc_config: null, compile_model: bool, compile_mode: string>
to
{'type': Value('string'), 'n_obs_steps': Value('int64'), 'input_features': {'observation.state': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.cam_high': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.cam_left_wrist': {'type': Value('string'), 'shape': List(Value('int64'))}, 'observation.images.cam_right_wrist': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'output_features': {'action': {'type': Value('string'), 'shape': List(Value('int64'))}}, 'device': Value('string'), 'use_amp': Value('bool'), 'use_peft': Value('bool'), 'push_to_hub': Value('bool'), 'repo_id': Value('null'), 'private': Value('null'), 'tags': Value('null'), 'license': Value('null'), 'pretrained_path': Value('null'), 'horizon': Value('int64'), 'n_action_steps': Value('int64'), 'normalization_mapping': {'VISUAL': Value('string'), 'STATE': Value('string'), 'ACTION': Value('string')}, 'drop_n_last_frames': Value('int64'), 'vision_backbone': Value('string'), 'resize_shape': Value('null'), 'crop_ratio': Value('float64'), 'crop_shape': Value('null'), 'crop_is_random': Value('bool'), 'pretrained_backbone_weights': Value('null'), 'use_group_norm': Value('bool'), 'spatial_softmax_num_keypoints': Value('int64'), 'use_separate_rgb_encoder_per_camera': Value('bool'), 'down_dims': List(Value('int64')), 'kernel_size': Value('int64'), 'n_groups': Value('int64'), 'diffusion_step_embed_dim': Value('int64'), 'use_film_scale_modulation': Value('bool'), 'noise_scheduler_type': Value('string'), 'num_train_timesteps': Value('int64'), 'beta_schedule': Value('string'), 'beta_start': Value('float64'), 'beta_end': Value('float64'), 'prediction_type': Value('string'), 'clip_sample': Value('bool'), 'clip_sample_range': Value('float64'), 'num_inference_steps': Value('int64'), 'compile_model': Value('bool'), 'compile_mode': Value('string'), 'do_mask_loss_for_padding': Value('bool'), 'optimizer_lr': Value('float64'), 'optimizer_betas': List(Value('float64')), 'optimizer_eps': Value('float64'), 'optimizer_weight_decay': Value('float64'), 'scheduler_name': Value('string'), 'scheduler_warmup_steps': Value('int64')}Need 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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CTR original experiment evidence: pro-7901d0dfa49c
User-authorized public archive. evidence-index.json distinguishes newly preserved files from byte-identical source evidence already saved at the fixed pro-790182b14f35 archive revision. Restore new-evidence.tar with restore_large_files.py. Original checkpoint weights/normalization match fixed public model packages; model-configs retains actual source training/loading configurations, including publication-only offline loader differences for E261/E263. Scientific result lifecycle and known defects are unchanged.
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