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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
run_name: string
model_name: string
n_params: string
best_epoch: int64
best_val_f1: double
test_accuracy: double
test_macro_f1: double
test_far: double
test_frr: double
per_class_far: struct<hey_ava: double, hey_t_mobile: double, hey_turkcell: double, hey_cassandra: double, hey_dtag: (... 8 chars omitted)
  child 0, hey_ava: double
  child 1, hey_t_mobile: double
  child 2, hey_turkcell: double
  child 3, hey_cassandra: double
  child 4, hey_dtag: double
per_class_frr: struct<hey_ava: double, hey_t_mobile: double, hey_turkcell: double, hey_cassandra: double, hey_dtag: (... 8 chars omitted)
  child 0, hey_ava: double
  child 1, hey_t_mobile: double
  child 2, hey_turkcell: double
  child 3, hey_cassandra: double
  child 4, hey_dtag: double
confusion_matrix: list<item: list<item: int64>>
  child 0, item: list<item: int64>
      child 0, item: int64
classification_report: string
args: struct<epochs: int64, batch_size: int64, lr: double, embedding_dim: int64, dropout: double, num_head (... 245 chars omitted)
  child 0, epochs: int64
  child 1, batch_size: int64
  child 2, lr: double
  child 3, embedding_dim: int64
  child 4, dropout: double
  child 5, num_heads: int64
  child 6, focal_gamma: double
  child 7, mixup_alpha: double
  child 8, cutmix_alpha: double
  child 9, cosine_t0: int64
  child 10, cosine_t_mult: int64
  child 11, hard_neg_buffer: int64
  child 12, hard_neg_thresh: double
  child 13, patience: int64
  child 14, num_workers: int64
  child 15, seed: int64
  child 16, single_gpu: int64
  child 17, port: string
class_names: list<item: string>
  child 0, item: string
val_frr: list<item: double>
  child 0, item: double
epoch: list<item: int64>
  child 0, item: int64
lr: list<item: double>
  child 0, item: double
train_acc: list<item: double>
  child 0, item: double
val_f1: list<item: double>
  child 0, item: double
val_acc: list<item: double>
  child 0, item: double
val_loss: list<item: double>
  child 0, item: double
val_far: list<item: double>
  child 0, item: double
train_loss: list<item: double>
  child 0, item: double
to
{'epoch': List(Value('int64')), 'train_loss': List(Value('float64')), 'train_acc': List(Value('float64')), 'val_loss': List(Value('float64')), 'val_acc': List(Value('float64')), 'val_f1': List(Value('float64')), 'val_far': List(Value('float64')), 'val_frr': List(Value('float64')), 'lr': List(Value('float64'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                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
              run_name: string
              model_name: string
              n_params: string
              best_epoch: int64
              best_val_f1: double
              test_accuracy: double
              test_macro_f1: double
              test_far: double
              test_frr: double
              per_class_far: struct<hey_ava: double, hey_t_mobile: double, hey_turkcell: double, hey_cassandra: double, hey_dtag: (... 8 chars omitted)
                child 0, hey_ava: double
                child 1, hey_t_mobile: double
                child 2, hey_turkcell: double
                child 3, hey_cassandra: double
                child 4, hey_dtag: double
              per_class_frr: struct<hey_ava: double, hey_t_mobile: double, hey_turkcell: double, hey_cassandra: double, hey_dtag: (... 8 chars omitted)
                child 0, hey_ava: double
                child 1, hey_t_mobile: double
                child 2, hey_turkcell: double
                child 3, hey_cassandra: double
                child 4, hey_dtag: double
              confusion_matrix: list<item: list<item: int64>>
                child 0, item: list<item: int64>
                    child 0, item: int64
              classification_report: string
              args: struct<epochs: int64, batch_size: int64, lr: double, embedding_dim: int64, dropout: double, num_head (... 245 chars omitted)
                child 0, epochs: int64
                child 1, batch_size: int64
                child 2, lr: double
                child 3, embedding_dim: int64
                child 4, dropout: double
                child 5, num_heads: int64
                child 6, focal_gamma: double
                child 7, mixup_alpha: double
                child 8, cutmix_alpha: double
                child 9, cosine_t0: int64
                child 10, cosine_t_mult: int64
                child 11, hard_neg_buffer: int64
                child 12, hard_neg_thresh: double
                child 13, patience: int64
                child 14, num_workers: int64
                child 15, seed: int64
                child 16, single_gpu: int64
                child 17, port: string
              class_names: list<item: string>
                child 0, item: string
              val_frr: list<item: double>
                child 0, item: double
              epoch: list<item: int64>
                child 0, item: int64
              lr: list<item: double>
                child 0, item: double
              train_acc: list<item: double>
                child 0, item: double
              val_f1: list<item: double>
                child 0, item: double
              val_acc: list<item: double>
                child 0, item: double
              val_loss: list<item: double>
                child 0, item: double
              val_far: list<item: double>
                child 0, item: double
              train_loss: list<item: double>
                child 0, item: double
              to
              {'epoch': List(Value('int64')), 'train_loss': List(Value('float64')), 'train_acc': List(Value('float64')), 'val_loss': List(Value('float64')), 'val_acc': List(Value('float64')), 'val_f1': List(Value('float64')), 'val_far': List(Value('float64')), 'val_frr': List(Value('float64')), 'lr': List(Value('float64'))}
              because column names don't match
              
              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 1347, 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 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, 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 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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epoch
list
train_loss
list
train_acc
list
val_loss
list
val_acc
list
val_f1
list
val_far
list
val_frr
list
lr
list
[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ]
[ 0.5089186223927941, 0.5079592704096672, 0.5079361379899703, 0.5079197311290704, 0.507888638421455, 0.5078568547994836, 0.507841204999033, 0.5078368117524644, 0.5078120109652539, 0.5078068754917694, 0.5079417400872849, 0.5079604534973122, 0.5079326448611465, 0.5079439199988285, 0.50792385...
[ 71.48769921089277, 72.82173526226211, 72.82769869513642, 72.84188199494558, 72.81013074423642, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.81432126463459, 72.86347929238228, 72.78772757749239, 72.84994068801898, 72.84816777554283, 7...
[ 0.5078213921818796, 0.5078061159835396, 0.5078027595830436, 0.5077764186086041, 0.507795118164308, 0.5077809013500072, 0.5077789777232082, 0.5077743728463799, 0.5077709429236529, 0.5077690220754533, 0.5078863813587935, 0.5077990599640525, 0.5078714346826667, 0.5077777711963496, 0.5077813...
[ 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 7...
[ 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.05050...
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100 ]
[ 0.0009755282581475768, 0.0009045084971874737, 0.0007938926261462366, 0.0006545084971874737, 0.0005, 0.00034549150281252633, 0.00020610737385376348, 0.00009549150281252633, 0.000024471741852423235, 0.001, 0.0009938441702975688, 0.0009755282581475768, 0.0009455032620941839, 0.000904508497187...
[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 ]
[ 0.47218308274452836, 0.4706244755093709, 0.4703848210682865, 0.47033984201158036, 0.4703007429629171, 0.47034258095443054, 0.470328597142899, 0.47027971463357854, 0.4702970359179348, 0.4703107499963183, 0.47031972249324366, 0.47030342123763197, 0.4702945245251601, 0.4703081351806327, 0.4...
[ 72.83624090979421, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 72.86347929238228, 7...
[ 0.47114548175641807, 0.47034840393971294, 0.4702645494501189, 0.4702642902289287, 0.47026658471268, 0.47026322359102396, 0.4702633531770297, 0.47026583055655163, 0.47027120589226384, 0.4702714889651478, 0.47026401384435473, 0.4702697710038805, 0.4702743174809434, 0.47027293521382235, 0.4...
[ 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 72.86535833096939, 7...
[ 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.050503242232566, 14.05050...
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100 ]
[ 0.000059999999999999995, 0.00008999999999999999, 0.00011999999999999999, 0.00015, 0.00017999999999999998, 0.00020999999999999998, 0.00023999999999999998, 0.00027, 0.0003, 0.0003, 0.000299980179223908, 0.00029992072231443425, 0.00029982164552650413, 0.0002996829759467223, 0.00029950475148...

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