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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
return array_cast(
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2005, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1348, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 890, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 951, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | level int64 | template_id int64 | template_type string | hides list | question string | options unknown | answer int64 | acceptance_bounds dict | provenance dict | context dict |
|---|---|---|---|---|---|---|---|---|---|---|
bc83aa2e-25f3-4c5b-ba3f-21b33b3b3bdb | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 52 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 296
} | {
"dataset": "aursad",
"episode": "experiment_100",
"subseries_start_index": 57,
"subseries_length": 47,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 47,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
e1500706-4614-4a1f-869d-c4561f0bc0ea | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 64 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 295
} | {
"dataset": "aursad",
"episode": "experiment_1003",
"subseries_start_index": 4,
"subseries_length": 59,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 59,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
65bace2b-1cd1-4dfd-b56f-549919028994 | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 42 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 297
} | {
"dataset": "aursad",
"episode": "experiment_1007",
"subseries_start_index": 27,
"subseries_length": 37,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 37,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
bb70e981-3d4e-4b65-844d-2ae3d0efabdf | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 63 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 297
} | {
"dataset": "aursad",
"episode": "experiment_1011",
"subseries_start_index": 28,
"subseries_length": 58,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 58,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
b5cb02ae-8ecd-4813-8c94-a67ef7308066 | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 22 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 296
} | {
"dataset": "aursad",
"episode": "experiment_1015",
"subseries_start_index": 103,
"subseries_length": 49,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 17,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
4a39ce6e-67a7-4f23-9173-26d77fd139d6 | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the lift of the object in the robot's time series. Assuming a fixed window length of 37 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 1,680 | {
"min": 1384,
"max": 1977
} | {
"dataset": "aursad",
"episode": "experiment_1015",
"subseries_start_index": 103,
"subseries_length": 49,
"phase_name": "4",
"phase_start_in_subseries": 17,
"phase_length": 32,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
fee92993-e86d-449b-b94a-8813ddb67327 | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 64 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 296
} | {
"dataset": "aursad",
"episode": "experiment_1017",
"subseries_start_index": 43,
"subseries_length": 59,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 59,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
d565b976-30e7-4715-b526-a809446b630a | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 46 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 297
} | {
"dataset": "aursad",
"episode": "experiment_102",
"subseries_start_index": 55,
"subseries_length": 41,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 41,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
353af895-c56c-4504-9608-3e9d708f9b3f | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 43 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 297
} | {
"dataset": "aursad",
"episode": "experiment_1023",
"subseries_start_index": 11,
"subseries_length": 38,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 38,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
c79e04dc-0172-4d1a-a066-82f305d5f3bc | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 43 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 297
} | {
"dataset": "aursad",
"episode": "experiment_1025",
"subseries_start_index": 45,
"subseries_length": 38,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 38,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
4ab4ca4e-8c8a-4972-bede-8a21734941ea | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the approach to the object in the robot's time series. Assuming a fixed window length of 44 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 0 | {
"min": 0,
"max": 297
} | {
"dataset": "aursad",
"episode": "experiment_1031",
"subseries_start_index": 48,
"subseries_length": 39,
"phase_name": "0",
"phase_start_in_subseries": 0,
"phase_length": 39,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
4392f220-8fc6-4bb9-81e2-97b6abb76770 | 1 | 1 | predictive | [] | The robot is performing a manipulation task. We want to isolate the lift of the object in the robot's time series. Assuming a fixed window length of 17 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else. | {} | 3,756 | {
"min": 3460,
"max": 4053
} | {
"dataset": "aursad",
"episode": "experiment_1033",
"subseries_start_index": 80,
"subseries_length": 50,
"phase_name": "4",
"phase_start_in_subseries": 38,
"phase_length": 12,
"relevance": {
"fault_id": 0,
"sampler": "uniform",
"locality": "global",
"validated": true
}
} | {
"time_series_format": {
"description": "Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.",
"acronym_mapping": {
"ett0": "effort_target_torque_0",
"ett1": "effort_target_torque_1",
"ett2... |
End of preview.