Datasets:

---

tp140205

/

jepa-artifacts

Like 0

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<script_path: string, folder_name: string, data_path: string, data_set: string, model_name: string, using_embedding: bool, emb_train: string, emb_test: string, use_split_assignments: bool, exp_train_total_epochs: int64, split_config_file: string, sigma_aggregation: string, lr: double, weight_decay: double, batch_size: int64, exp_patience: int64, encoder_type: string, hidden_dim: int64, n_hidden_layers: int64, n_cross_layers: int64, hidden_dropout: double, cross_dropout: double, d_embedding: int64, stacked: bool>
to
{'script_path': Value('string'), 'folder_name': Value('string'), 'data_path': Value('string'), 'data_set': Value('string'), 'model_name': Value('string'), 'using_embedding': Value('bool'), 'emb_train': Value('string'), 'emb_test': Value('string'), 'use_split_assignments': Value('bool'), 'exp_train_total_epochs': Value('int64'), 'split_config_file': Value('string'), 'sigma_aggregation': Value('string'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'batch_size': Value('int64'), 'exp_patience': Value('int64'), 'encoder_type': Value('string'), 'activation': Value('string'), 'initialization': Value('string'), 'n_layers': Value('int64'), 'd_token': Value('int64'), 'n_heads': Value('int64'), 'attention_dropout': Value('float64'), 'residual_dropout': Value('float64'), 'prenormalization': Value('bool')}
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 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<script_path: string, folder_name: string, data_path: string, data_set: string, model_name: string, using_embedding: bool, emb_train: string, emb_test: string, use_split_assignments: bool, exp_train_total_epochs: int64, split_config_file: string, sigma_aggregation: string, lr: double, weight_decay: double, batch_size: int64, exp_patience: int64, encoder_type: string, hidden_dim: int64, n_hidden_layers: int64, n_cross_layers: int64, hidden_dropout: double, cross_dropout: double, d_embedding: int64, stacked: bool>
              to
              {'script_path': Value('string'), 'folder_name': Value('string'), 'data_path': Value('string'), 'data_set': Value('string'), 'model_name': Value('string'), 'using_embedding': Value('bool'), 'emb_train': Value('string'), 'emb_test': Value('string'), 'use_split_assignments': Value('bool'), 'exp_train_total_epochs': Value('int64'), 'split_config_file': Value('string'), 'sigma_aggregation': Value('string'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'batch_size': Value('int64'), 'exp_patience': Value('int64'), 'encoder_type': Value('string'), 'activation': Value('string'), 'initialization': Value('string'), 'n_layers': Value('int64'), 'd_token': Value('int64'), 'n_heads': Value('int64'), 'attention_dropout': Value('float64'), 'residual_dropout': Value('float64'), 'prenormalization': Value('bool')}