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_name: string
dataset_name: string
embedding_dim: int64
n_subjects: int64
n_classes: int64
mean_std: struct<accuracy: struct<mean: double, std: double>, macro_f1: struct<mean: double, std: double>, kap (... 38 chars omitted)
child 0, accuracy: struct<mean: double, std: double>
child 0, mean: double
child 1, std: double
child 1, macro_f1: struct<mean: double, std: double>
child 0, mean: double
child 1, std: double
child 2, kappa: struct<mean: double, std: double>
child 0, mean: double
child 1, std: double
per_fold: list<item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: stru (... 279 chars omitted)
child 0, item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: doubl (... 267 chars omitted)
child 0, fold: int64
child 1, accuracy: double
child 2, macro_f1: double
child 3, kappa: double
child 4, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
child 0, W: double
child 1, N1: double
child 2, N2: double
child 3, N3: double
child 4, REM: double
child 5, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
child 0, W: int64
child 1, N1: int64
child 2, N2: int64
child 3, N3: int64
child 4, REM: int64
child 6, n_train_subjects: int64
child 7, n_test_subjects: int64
child 8, n_train_epochs: int64
child 9, n_test_epochs: int64
child 10, confusion_matrix: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
class_names: list<item: string>
child 0, item: string
n_folds: int64
pooled: struct<accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: double, N1: double (... 160 chars omitted)
child 0, accuracy: double
child 1, macro_f1: double
child 2, kappa: double
child 3, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
child 0, W: double
child 1, N1: double
child 2, N2: double
child 3, N3: double
child 4, REM: double
child 4, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
child 0, W: int64
child 1, N1: int64
child 2, N2: int64
child 3, N3: int64
child 4, REM: int64
child 5, confusion_matrix: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
probe_config: struct<max_epochs: int64, lr: double, weight_decay: double, batch_size: int64>
child 0, max_epochs: int64
child 1, lr: double
child 2, weight_decay: double
child 3, batch_size: int64
to
{'model_name': Value('string'), 'dataset_name': Value('string'), 'n_folds': Value('int64'), 'n_classes': Value('int64'), 'class_names': List(Value('string')), 'n_subjects': Value('int64'), 'probe_config': {'max_epochs': Value('int64'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'batch_size': Value('int64')}, 'per_fold': List({'fold': Value('int64'), 'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'n_train_subjects': Value('int64'), 'n_test_subjects': Value('int64'), 'n_train_epochs': Value('int64'), 'n_test_epochs': Value('int64'), 'confusion_matrix': List(List(Value('int64')))}), 'pooled': {'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'confusion_matrix': List(List(Value('int64')))}, 'mean_std': {'accuracy': {'mean': Value('float64'), 'std': Value('float64')}, 'macro_f1': {'mean': Value('float64'), 'std': Value('float64')}, 'kappa': {'mean': Value('float64'), 'std': Value('float64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, 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 128, 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 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
model_name: string
dataset_name: string
embedding_dim: int64
n_subjects: int64
n_classes: int64
mean_std: struct<accuracy: struct<mean: double, std: double>, macro_f1: struct<mean: double, std: double>, kap (... 38 chars omitted)
child 0, accuracy: struct<mean: double, std: double>
child 0, mean: double
child 1, std: double
child 1, macro_f1: struct<mean: double, std: double>
child 0, mean: double
child 1, std: double
child 2, kappa: struct<mean: double, std: double>
child 0, mean: double
child 1, std: double
per_fold: list<item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: stru (... 279 chars omitted)
child 0, item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: doubl (... 267 chars omitted)
child 0, fold: int64
child 1, accuracy: double
child 2, macro_f1: double
child 3, kappa: double
child 4, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
child 0, W: double
child 1, N1: double
child 2, N2: double
child 3, N3: double
child 4, REM: double
child 5, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
child 0, W: int64
child 1, N1: int64
child 2, N2: int64
child 3, N3: int64
child 4, REM: int64
child 6, n_train_subjects: int64
child 7, n_test_subjects: int64
child 8, n_train_epochs: int64
child 9, n_test_epochs: int64
child 10, confusion_matrix: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
class_names: list<item: string>
child 0, item: string
n_folds: int64
pooled: struct<accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: double, N1: double (... 160 chars omitted)
child 0, accuracy: double
child 1, macro_f1: double
child 2, kappa: double
child 3, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
child 0, W: double
child 1, N1: double
child 2, N2: double
child 3, N3: double
child 4, REM: double
child 4, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
child 0, W: int64
child 1, N1: int64
child 2, N2: int64
child 3, N3: int64
child 4, REM: int64
child 5, confusion_matrix: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
probe_config: struct<max_epochs: int64, lr: double, weight_decay: double, batch_size: int64>
child 0, max_epochs: int64
child 1, lr: double
child 2, weight_decay: double
child 3, batch_size: int64
to
{'model_name': Value('string'), 'dataset_name': Value('string'), 'n_folds': Value('int64'), 'n_classes': Value('int64'), 'class_names': List(Value('string')), 'n_subjects': Value('int64'), 'probe_config': {'max_epochs': Value('int64'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'batch_size': Value('int64')}, 'per_fold': List({'fold': Value('int64'), 'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'n_train_subjects': Value('int64'), 'n_test_subjects': Value('int64'), 'n_train_epochs': Value('int64'), 'n_test_epochs': Value('int64'), 'confusion_matrix': List(List(Value('int64')))}), 'pooled': {'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'confusion_matrix': List(List(Value('int64')))}, 'mean_std': {'accuracy': {'mean': Value('float64'), 'std': Value('float64')}, 'macro_f1': {'mean': Value('float64'), 'std': Value('float64')}, 'kappa': {'mean': Value('float64'), 'std': Value('float64')}}}
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.
No dataset card yet
- Downloads last month
- 41