Datasets:

---

Prithxvhie44

/

vlm-results

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
key: string
boxes: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
scores: list<item: double>
  child 0, item: double
backend: string
ms: double
probs: struct<red: double, blue: double, green: double, yellow: double, black: double, white: double, orang (... 195 chars omitted)
  child 0, red: double
  child 1, blue: double
  child 2, green: double
  child 3, yellow: double
  child 4, black: double
  child 5, white: double
  child 6, orange: double
  child 7, brown: double
  child 8, gray: double
  child 9, purple: double
  child 10, pink: double
  child 11, wooden: double
  child 12, metal: double
  child 13, plastic: double
  child 14, glass: double
  child 15, leather: double
  child 16, stone: double
  child 17, fabric: double
  child 18, paper: double
seed: int64
results: struct<pope: struct<base_vlm: struct<random: struct<accuracy: double, precision: double, recall: dou (... 3896 chars omitted)
  child 0, pope: struct<base_vlm: struct<random: struct<accuracy: double, precision: double, recall: double, f1: doub (... 1098 chars omitted)
      child 0, base_vlm: struct<random: struct<accuracy: double, precision: double, recall: double, f1: double, status: strin (... 482 chars omitted)
          child 0, random: struct<accuracy: double, precision: double, recall: double, f1: double, status: string, sample_count (... 80 chars omitted)
              child 0, accuracy: double
              child 1, precision: double
              ch
...
upper: double
                  child 2, count: int64
                  child 3, mean_confidence: double
                  child 4, accuracy: double
  child 4, meta: struct<model: string, precision: string, hardware: string, consistent: bool, global_thresholds: list (... 301 chars omitted)
      child 0, model: string
      child 1, precision: string
      child 2, hardware: string
      child 3, consistent: bool
      child 4, global_thresholds: list<item: double>
          child 0, item: double
      child 5, grounding_threshold: double
      child 6, rerank_threshold: double
      child 7, thresholds: struct<attribute: list<item: double>, object: list<item: double>, quantity: list<item: double>, rela (... 25 chars omitted)
          child 0, attribute: list<item: double>
              child 0, item: double
          child 1, object: list<item: double>
              child 0, item: double
          child 2, quantity: list<item: double>
              child 0, item: double
          child 3, relation: list<item: double>
              child 0, item: double
      child 8, tuned: bool
      child 9, weights: struct<agreement: double, probability: double, verification: double>
          child 0, agreement: double
          child 1, probability: double
          child 2, verification: double
configuration: struct<proc_dir: string>
  child 0, proc_dir: string
environment: struct<python: string, platform: string>
  child 0, python: string
  child 1, platform: string
timestamp: string
to
{'timestamp': Value('string'), 'seed': Value('int64'), 'configuration': {'proc_dir': Value('string')}, 'environment': {'python': Value('string'), 'platform': Value('string')}, 'results': {'pope': {'base_vlm': {'random': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}, 'popular': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}, 'adversarial': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}}, 'full_toolkit': {'random': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}, 'popular': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), '
...
'accuracy': Value('float64')})}, 'pope_popular': {'available': Value('bool'), 'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'brier': Value('float64'), 'sample_count': Value('int64'), 'calibration_available': Value('bool'), 'ece': Value('float64'), 'reliability_diagram': List({'lower': Value('float64'), 'upper': Value('float64'), 'count': Value('int64'), 'mean_confidence': Value('float64'), 'accuracy': Value('float64')})}, 'pope_adversarial': {'available': Value('bool'), 'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'brier': Value('float64'), 'sample_count': Value('int64'), 'calibration_available': Value('bool'), 'ece': Value('float64'), 'reliability_diagram': List({'lower': Value('float64'), 'upper': Value('float64'), 'count': Value('int64'), 'mean_confidence': Value('float64'), 'accuracy': Value('float64')})}}, 'meta': {'model': Value('string'), 'precision': Value('string'), 'hardware': Value('string'), 'consistent': Value('bool'), 'global_thresholds': List(Value('float64')), 'grounding_threshold': Value('float64'), 'rerank_threshold': Value('float64'), 'thresholds': {'attribute': List(Value('float64')), 'object': List(Value('float64')), 'quantity': List(Value('float64')), 'relation': List(Value('float64'))}, 'tuned': Value('bool'), 'weights': {'agreement': Value('float64'), 'probability': Value('float64'), 'verification': Value('float64')}}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              key: string
              boxes: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              scores: list<item: double>
                child 0, item: double
              backend: string
              ms: double
              probs: struct<red: double, blue: double, green: double, yellow: double, black: double, white: double, orang (... 195 chars omitted)
                child 0, red: double
                child 1, blue: double
                child 2, green: double
                child 3, yellow: double
                child 4, black: double
                child 5, white: double
                child 6, orange: double
                child 7, brown: double
                child 8, gray: double
                child 9, purple: double
                child 10, pink: double
                child 11, wooden: double
                child 12, metal: double
                child 13, plastic: double
                child 14, glass: double
                child 15, leather: double
                child 16, stone: double
                child 17, fabric: double
                child 18, paper: double
              seed: int64
              results: struct<pope: struct<base_vlm: struct<random: struct<accuracy: double, precision: double, recall: dou (... 3896 chars omitted)
                child 0, pope: struct<base_vlm: struct<random: struct<accuracy: double, precision: double, recall: double, f1: doub (... 1098 chars omitted)
                    child 0, base_vlm: struct<random: struct<accuracy: double, precision: double, recall: double, f1: double, status: strin (... 482 chars omitted)
                        child 0, random: struct<accuracy: double, precision: double, recall: double, f1: double, status: string, sample_count (... 80 chars omitted)
                            child 0, accuracy: double
                            child 1, precision: double
                            ch
              ...
              upper: double
                                child 2, count: int64
                                child 3, mean_confidence: double
                                child 4, accuracy: double
                child 4, meta: struct<model: string, precision: string, hardware: string, consistent: bool, global_thresholds: list (... 301 chars omitted)
                    child 0, model: string
                    child 1, precision: string
                    child 2, hardware: string
                    child 3, consistent: bool
                    child 4, global_thresholds: list<item: double>
                        child 0, item: double
                    child 5, grounding_threshold: double
                    child 6, rerank_threshold: double
                    child 7, thresholds: struct<attribute: list<item: double>, object: list<item: double>, quantity: list<item: double>, rela (... 25 chars omitted)
                        child 0, attribute: list<item: double>
                            child 0, item: double
                        child 1, object: list<item: double>
                            child 0, item: double
                        child 2, quantity: list<item: double>
                            child 0, item: double
                        child 3, relation: list<item: double>
                            child 0, item: double
                    child 8, tuned: bool
                    child 9, weights: struct<agreement: double, probability: double, verification: double>
                        child 0, agreement: double
                        child 1, probability: double
                        child 2, verification: double
              configuration: struct<proc_dir: string>
                child 0, proc_dir: string
              environment: struct<python: string, platform: string>
                child 0, python: string
                child 1, platform: string
              timestamp: string
              to
              {'timestamp': Value('string'), 'seed': Value('int64'), 'configuration': {'proc_dir': Value('string')}, 'environment': {'python': Value('string'), 'platform': Value('string')}, 'results': {'pope': {'base_vlm': {'random': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}, 'popular': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}, 'adversarial': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}}, 'full_toolkit': {'random': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'status': Value('string'), 'sample_count': Value('int64'), 'yes_ratio': Value('float64'), 'abstention_rate': Value('float64'), 'corrected_yes_to_no': Value('int64')}, 'popular': {'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), '
              ...
              'accuracy': Value('float64')})}, 'pope_popular': {'available': Value('bool'), 'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'brier': Value('float64'), 'sample_count': Value('int64'), 'calibration_available': Value('bool'), 'ece': Value('float64'), 'reliability_diagram': List({'lower': Value('float64'), 'upper': Value('float64'), 'count': Value('int64'), 'mean_confidence': Value('float64'), 'accuracy': Value('float64')})}, 'pope_adversarial': {'available': Value('bool'), 'accuracy': Value('float64'), 'precision': Value('float64'), 'recall': Value('float64'), 'f1': Value('float64'), 'brier': Value('float64'), 'sample_count': Value('int64'), 'calibration_available': Value('bool'), 'ece': Value('float64'), 'reliability_diagram': List({'lower': Value('float64'), 'upper': Value('float64'), 'count': Value('int64'), 'mean_confidence': Value('float64'), 'accuracy': Value('float64')})}}, 'meta': {'model': Value('string'), 'precision': Value('string'), 'hardware': Value('string'), 'consistent': Value('bool'), 'global_thresholds': List(Value('float64')), 'grounding_threshold': Value('float64'), 'rerank_threshold': Value('float64'), 'thresholds': {'attribute': List(Value('float64')), 'object': List(Value('float64')), 'quantity': List(Value('float64')), 'relation': List(Value('float64'))}, 'tuned': Value('bool'), 'weights': {'agreement': Value('float64'), 'probability': Value('float64'), 'verification': Value('float64')}}}}
              because column names don't match