Datasets:

---

multabench

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embeddings-dino-small-tar

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MulTaBench 4

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dataset: string
encoder: string
model: string
size: string
fold: int64
n_train: int64
n_test: int64
lora: struct<learning_rate: double, epochs: int64, lora_rank: int64, layers: int64, patience: int64, weigh (... 35 chars omitted)
  child 0, learning_rate: double
  child 1, epochs: int64
  child 2, lora_rank: int64
  child 3, layers: int64
  child 4, patience: int64
  child 5, weight_decay: double
  child 6, batch_size: int64
best_val_loss: double
epochs: double
n_rows: int64
n_inputs: int64
n_columns: int64
features: list<item: string>
  child 0, item: string
finetune_seconds: double
encode_seconds: struct<image file path: double>
  child 0, image file path: double
total_seconds: double
git: string
timestamp: timestamp[s]
gpu_type: string
gpu_total_memory_gb: double
use_gpu: bool
cpu_cores: int64
system_ram_gb: double
cpu_name: string
visible_cpus: int64
ram_limit_gb: double
expected_hardware: struct<gpu_prefix: string, cpu_prefix: null, cpus: int64, ram_gb: int64>
  child 0, gpu_prefix: string
  child 1, cpu_prefix: null
  child 2, cpus: int64
  child 3, ram_gb: int64
official_hardware_mismatches: list<item: string>
  child 0, item: string
eva_config: null
trainable_token_indices: null
peft_version: string
arrow_config: null
megatron_config: null
base_model_name_or_path: string
use_qalora: bool
rank_pattern: struct<>
exclude_modules: null
target_modules: list<item: string>
  child 0, item: string
auto_mapping: struct<base_model_class: string, parent_library: string>
  child 0, base_model_class: string
  child 1, parent_library: string
ensure_weight_tying: bool
task_type: null
megatron_core: string
corda_config: null
lora_alpha: int64
peft_type: string
use_dora: bool
lora_bias: bool
alora_invocation_tokens: null
qalora_group_size: int64
lora_dropout: double
use_rslora: bool
modules_to_save: null
loftq_config: struct<>
alpha_pattern: struct<>
r: int64
revision: null
layers_to_transform: null
target_parameters: null
inference_mode: bool
bias: string
init_lora_weights: bool
layer_replication: null
layers_pattern: null
fan_in_fan_out: bool
to
{'alora_invocation_tokens': Value('null'), 'alpha_pattern': {}, 'arrow_config': Value('null'), 'auto_mapping': {'base_model_class': Value('string'), 'parent_library': Value('string')}, 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'corda_config': Value('null'), 'ensure_weight_tying': Value('bool'), 'eva_config': Value('null'), 'exclude_modules': Value('null'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_bias': Value('bool'), 'lora_dropout': Value('float64'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': Value('null'), 'peft_type': Value('string'), 'peft_version': Value('string'), 'qalora_group_size': Value('int64'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('null'), 'target_modules': List(Value('string')), 'target_parameters': Value('null'), 'task_type': Value('null'), 'trainable_token_indices': Value('null'), 'use_dora': Value('bool'), 'use_qalora': Value('bool'), 'use_rslora': Value('bool')}
because column names don't match
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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              dataset: string
              encoder: string
              model: string
              size: string
              fold: int64
              n_train: int64
              n_test: int64
              lora: struct<learning_rate: double, epochs: int64, lora_rank: int64, layers: int64, patience: int64, weigh (... 35 chars omitted)
                child 0, learning_rate: double
                child 1, epochs: int64
                child 2, lora_rank: int64
                child 3, layers: int64
                child 4, patience: int64
                child 5, weight_decay: double
                child 6, batch_size: int64
              best_val_loss: double
              epochs: double
              n_rows: int64
              n_inputs: int64
              n_columns: int64
              features: list<item: string>
                child 0, item: string
              finetune_seconds: double
              encode_seconds: struct<image file path: double>
                child 0, image file path: double
              total_seconds: double
              git: string
              timestamp: timestamp[s]
              gpu_type: string
              gpu_total_memory_gb: double
              use_gpu: bool
              cpu_cores: int64
              system_ram_gb: double
              cpu_name: string
              visible_cpus: int64
              ram_limit_gb: double
              expected_hardware: struct<gpu_prefix: string, cpu_prefix: null, cpus: int64, ram_gb: int64>
                child 0, gpu_prefix: string
                child 1, cpu_prefix: null
                child 2, cpus: int64
                child 3, ram_gb: int64
              official_hardware_mismatches: list<item: string>
                child 0, item: string
              eva_config: null
              trainable_token_indices: null
              peft_version: string
              arrow_config: null
              megatron_config: null
              base_model_name_or_path: string
              use_qalora: bool
              rank_pattern: struct<>
              exclude_modules: null
              target_modules: list<item: string>
                child 0, item: string
              auto_mapping: struct<base_model_class: string, parent_library: string>
                child 0, base_model_class: string
                child 1, parent_library: string
              ensure_weight_tying: bool
              task_type: null
              megatron_core: string
              corda_config: null
              lora_alpha: int64
              peft_type: string
              use_dora: bool
              lora_bias: bool
              alora_invocation_tokens: null
              qalora_group_size: int64
              lora_dropout: double
              use_rslora: bool
              modules_to_save: null
              loftq_config: struct<>
              alpha_pattern: struct<>
              r: int64
              revision: null
              layers_to_transform: null
              target_parameters: null
              inference_mode: bool
              bias: string
              init_lora_weights: bool
              layer_replication: null
              layers_pattern: null
              fan_in_fan_out: bool
              to
              {'alora_invocation_tokens': Value('null'), 'alpha_pattern': {}, 'arrow_config': Value('null'), 'auto_mapping': {'base_model_class': Value('string'), 'parent_library': Value('string')}, 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'corda_config': Value('null'), 'ensure_weight_tying': Value('bool'), 'eva_config': Value('null'), 'exclude_modules': Value('null'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_bias': Value('bool'), 'lora_dropout': Value('float64'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': Value('null'), 'peft_type': Value('string'), 'peft_version': Value('string'), 'qalora_group_size': Value('int64'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('null'), 'target_modules': List(Value('string')), 'target_parameters': Value('null'), 'task_type': Value('null'), 'trainable_token_indices': Value('null'), 'use_dora': Value('bool'), 'use_qalora': Value('bool'), 'use_rslora': Value('bool')}
              because column names don't match