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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
format: string
environment: struct<python: string, platform: string, torch: string, cuda: string, transformers: string, tf32_mat (... 125 chars omitted)
child 0, python: string
child 1, platform: string
child 2, torch: string
child 3, cuda: string
child 4, transformers: string
child 5, tf32_matmul: bool
child 6, tf32_cudnn: bool
child 7, autocast: bool
child 8, gpu: string
child 9, gpu_uuid: string
child 10, vram_bytes: int64
child 11, capability: list<item: int64>
child 0, item: int64
weights_sha256: string
architecture_sha256: string
files: struct<cases.pt: struct<sha256: string, size_bytes: int64>, golden.pt: struct<sha256: string, size_b (... 13 chars omitted)
child 0, cases.pt: struct<sha256: string, size_bytes: int64>
child 0, sha256: string
child 1, size_bytes: int64
child 1, golden.pt: struct<sha256: string, size_bytes: int64>
child 0, sha256: string
child 1, size_bytes: int64
levels: list<item: struct<level: int64, temporal_stride: int64, temporal_window_size: int64, target_length: (... 1847 chars omitted)
child 0, item: struct<level: int64, temporal_stride: int64, temporal_window_size: int64, target_length: int64, acti (... 1835 chars omitted)
child 0, level: int64
child 1, temporal_stride: int64
child 2, temporal_window_size: int64
child 3, target_length: int64
child 4, action_queue_size: int64
child 5, encoder: struct<kind: string, vit_config: struct<transformers_versi
...
input_dim: int64
child 1, hidden_dim: int64
child 2, output_dim: int64
child 3, normalization: string
child 4, normalization_eps: double
child 5, final_ln: bool
child 8, action_embed: struct<input_dim: int64, smoothed_dim: int64, emb_dim: int64, mlp_scale: int64, patch_embed: bool>
child 0, input_dim: int64
child 1, smoothed_dim: int64
child 2, emb_dim: int64
child 3, mlp_scale: int64
child 4, patch_embed: bool
child 9, action_encoder: struct<kind: string, settings: struct<output_dim: int64, input_dim: int64, d_model: int64, nhead: in (... 93 chars omitted)
child 0, kind: string
child 1, settings: struct<output_dim: int64, input_dim: int64, d_model: int64, nhead: int64, num_layers: int64, ff_mult (... 61 chars omitted)
child 0, output_dim: int64
child 1, input_dim: int64
child 2, d_model: int64
child 3, nhead: int64
child 4, num_layers: int64
child 5, ff_mult: int64
child 6, dropout: double
child 7, max_chunk: int64
child 8, norm_first: bool
num_levels: int64
embed_dims: struct<1: int64, 2: int64, 3: int64>
child 0, 1: int64
child 1, 2: int64
child 2, 3: int64
source_commit: string
source_checkpoint_sha256: string
latent_dim: int64
patch_size: int64
scope: string
precision: string
proprio_dim: int64
image_size: int64
to
{'format': Value('string'), 'source_checkpoint_sha256': Value('string'), 'source_commit': Value('string'), 'num_levels': Value('int64'), 'latent_dim': Value('int64'), 'embed_dims': {'1': Value('int64'), '2': Value('int64'), '3': Value('int64')}, 'image_size': Value('int64'), 'patch_size': Value('int64'), 'proprio_dim': Value('int64'), 'levels': List({'level': Value('int64'), 'temporal_stride': Value('int64'), 'temporal_window_size': Value('int64'), 'target_length': Value('int64'), 'action_queue_size': Value('int64'), 'encoder': {'kind': Value('string'), 'vit_config': {'transformers_version': Value('string'), 'architectures': Value('null'), 'output_hidden_states': Value('bool'), 'return_dict': Value('bool'), 'dtype': Value('null'), 'chunk_size_feed_forward': Value('int64'), 'is_encoder_decoder': Value('bool'), 'id2label': {'0': Value('string'), '1': Value('string')}, 'label2id': {'LABEL_0': Value('int64'), 'LABEL_1': Value('int64')}, 'problem_type': Value('null'), 'hidden_size': Value('int64'), 'num_hidden_layers': Value('int64'), 'num_attention_heads': Value('int64'), 'intermediate_size': Value('int64'), 'hidden_act': Value('string'), 'hidden_dropout_prob': Value('float64'), 'attention_probs_dropout_prob': Value('float64'), 'initializer_range': Value('float64'), 'layer_norm_eps': Value('float64'), 'image_size': Value('int64'), 'patch_size': Value('int64'), 'num_channels': Value('int64'), 'qkv_bias': Value('bool'), 'encoder_stride': Value('int64'), 'pooler_output_size': Value(
...
ue('string'), 'add_pooling_layer': Value('bool'), 'use_mask_token': Value('bool'), 'projector': {'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'normalization': Value('string'), 'normalization_eps': Value('float64'), 'final_ln': Value('bool')}, 'mlp': {'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'final_ln': Value('bool'), 'trunc_normal_init': Value('bool')}}, 'predictor': {'num_frames': Value('int64'), 'depth': Value('int64'), 'heads': Value('int64'), 'mlp_dim': Value('int64'), 'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'dim_head': Value('int64'), 'dropout': Value('float64'), 'emb_dropout': Value('float64')}, 'predictor_projector': {'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'normalization': Value('string'), 'normalization_eps': Value('float64'), 'final_ln': Value('bool')}, 'action_embed': {'input_dim': Value('int64'), 'smoothed_dim': Value('int64'), 'emb_dim': Value('int64'), 'mlp_scale': Value('int64'), 'patch_embed': Value('bool')}, 'action_encoder': {'kind': Value('string'), 'settings': {'output_dim': Value('int64'), 'input_dim': Value('int64'), 'd_model': Value('int64'), 'nhead': Value('int64'), 'num_layers': Value('int64'), 'ff_mult': Value('int64'), 'dropout': Value('float64'), 'max_chunk': Value('int64'), 'norm_first': Value('bool')}}}), 'scope': Value('string'), 'precision': 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
format: string
environment: struct<python: string, platform: string, torch: string, cuda: string, transformers: string, tf32_mat (... 125 chars omitted)
child 0, python: string
child 1, platform: string
child 2, torch: string
child 3, cuda: string
child 4, transformers: string
child 5, tf32_matmul: bool
child 6, tf32_cudnn: bool
child 7, autocast: bool
child 8, gpu: string
child 9, gpu_uuid: string
child 10, vram_bytes: int64
child 11, capability: list<item: int64>
child 0, item: int64
weights_sha256: string
architecture_sha256: string
files: struct<cases.pt: struct<sha256: string, size_bytes: int64>, golden.pt: struct<sha256: string, size_b (... 13 chars omitted)
child 0, cases.pt: struct<sha256: string, size_bytes: int64>
child 0, sha256: string
child 1, size_bytes: int64
child 1, golden.pt: struct<sha256: string, size_bytes: int64>
child 0, sha256: string
child 1, size_bytes: int64
levels: list<item: struct<level: int64, temporal_stride: int64, temporal_window_size: int64, target_length: (... 1847 chars omitted)
child 0, item: struct<level: int64, temporal_stride: int64, temporal_window_size: int64, target_length: int64, acti (... 1835 chars omitted)
child 0, level: int64
child 1, temporal_stride: int64
child 2, temporal_window_size: int64
child 3, target_length: int64
child 4, action_queue_size: int64
child 5, encoder: struct<kind: string, vit_config: struct<transformers_versi
...
input_dim: int64
child 1, hidden_dim: int64
child 2, output_dim: int64
child 3, normalization: string
child 4, normalization_eps: double
child 5, final_ln: bool
child 8, action_embed: struct<input_dim: int64, smoothed_dim: int64, emb_dim: int64, mlp_scale: int64, patch_embed: bool>
child 0, input_dim: int64
child 1, smoothed_dim: int64
child 2, emb_dim: int64
child 3, mlp_scale: int64
child 4, patch_embed: bool
child 9, action_encoder: struct<kind: string, settings: struct<output_dim: int64, input_dim: int64, d_model: int64, nhead: in (... 93 chars omitted)
child 0, kind: string
child 1, settings: struct<output_dim: int64, input_dim: int64, d_model: int64, nhead: int64, num_layers: int64, ff_mult (... 61 chars omitted)
child 0, output_dim: int64
child 1, input_dim: int64
child 2, d_model: int64
child 3, nhead: int64
child 4, num_layers: int64
child 5, ff_mult: int64
child 6, dropout: double
child 7, max_chunk: int64
child 8, norm_first: bool
num_levels: int64
embed_dims: struct<1: int64, 2: int64, 3: int64>
child 0, 1: int64
child 1, 2: int64
child 2, 3: int64
source_commit: string
source_checkpoint_sha256: string
latent_dim: int64
patch_size: int64
scope: string
precision: string
proprio_dim: int64
image_size: int64
to
{'format': Value('string'), 'source_checkpoint_sha256': Value('string'), 'source_commit': Value('string'), 'num_levels': Value('int64'), 'latent_dim': Value('int64'), 'embed_dims': {'1': Value('int64'), '2': Value('int64'), '3': Value('int64')}, 'image_size': Value('int64'), 'patch_size': Value('int64'), 'proprio_dim': Value('int64'), 'levels': List({'level': Value('int64'), 'temporal_stride': Value('int64'), 'temporal_window_size': Value('int64'), 'target_length': Value('int64'), 'action_queue_size': Value('int64'), 'encoder': {'kind': Value('string'), 'vit_config': {'transformers_version': Value('string'), 'architectures': Value('null'), 'output_hidden_states': Value('bool'), 'return_dict': Value('bool'), 'dtype': Value('null'), 'chunk_size_feed_forward': Value('int64'), 'is_encoder_decoder': Value('bool'), 'id2label': {'0': Value('string'), '1': Value('string')}, 'label2id': {'LABEL_0': Value('int64'), 'LABEL_1': Value('int64')}, 'problem_type': Value('null'), 'hidden_size': Value('int64'), 'num_hidden_layers': Value('int64'), 'num_attention_heads': Value('int64'), 'intermediate_size': Value('int64'), 'hidden_act': Value('string'), 'hidden_dropout_prob': Value('float64'), 'attention_probs_dropout_prob': Value('float64'), 'initializer_range': Value('float64'), 'layer_norm_eps': Value('float64'), 'image_size': Value('int64'), 'patch_size': Value('int64'), 'num_channels': Value('int64'), 'qkv_bias': Value('bool'), 'encoder_stride': Value('int64'), 'pooler_output_size': Value(
...
ue('string'), 'add_pooling_layer': Value('bool'), 'use_mask_token': Value('bool'), 'projector': {'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'normalization': Value('string'), 'normalization_eps': Value('float64'), 'final_ln': Value('bool')}, 'mlp': {'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'final_ln': Value('bool'), 'trunc_normal_init': Value('bool')}}, 'predictor': {'num_frames': Value('int64'), 'depth': Value('int64'), 'heads': Value('int64'), 'mlp_dim': Value('int64'), 'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'dim_head': Value('int64'), 'dropout': Value('float64'), 'emb_dropout': Value('float64')}, 'predictor_projector': {'input_dim': Value('int64'), 'hidden_dim': Value('int64'), 'output_dim': Value('int64'), 'normalization': Value('string'), 'normalization_eps': Value('float64'), 'final_ln': Value('bool')}, 'action_embed': {'input_dim': Value('int64'), 'smoothed_dim': Value('int64'), 'emb_dim': Value('int64'), 'mlp_scale': Value('int64'), 'patch_embed': Value('bool')}, 'action_encoder': {'kind': Value('string'), 'settings': {'output_dim': Value('int64'), 'input_dim': Value('int64'), 'd_model': Value('int64'), 'nhead': Value('int64'), 'num_layers': Value('int64'), 'ff_mult': Value('int64'), 'dropout': Value('float64'), 'max_chunk': Value('int64'), 'norm_first': Value('bool')}}}), 'scope': Value('string'), 'precision': 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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