Datasets:

---

RMhasan

/

bangla-preference-dataset

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
category: string
chosen: list<item: struct<content: string, role: string>>
  child 0, item: struct<content: string, role: string>
      child 0, content: string
      child 1, role: string
duplicate_group_size: int64
id: string
kendalls_w: int64
loser_model: string
prompt: list<item: struct<content: string, role: string>>
  child 0, item: struct<content: string, role: string>
      child 0, content: string
      child 1, role: string
prompt_text: string
ranking_status: string
rejected: list<item: struct<content: string, role: string>>
  child 0, item: struct<content: string, role: string>
      child 0, content: string
      child 1, role: string
source_id: string
winner_model: string
train_categories: struct<economics: int64, education: int64, history: int64, mathematics: int64, moral and ethics: int (... 18 chars omitted)
  child 0, economics: int64
  child 1, education: int64
  child 2, history: int64
  child 3, mathematics: int64
  child 4, moral and ethics: int64
  child 5, sports: int64
filter: struct<development_prompts_per_category: int64, maximum_chosen_chars: int64, maximum_chosen_to_rejec (... 241 chars omitted)
  child 0, development_prompts_per_category: int64
  child 1, maximum_chosen_chars: int64
  child 2, maximum_chosen_to_rejected_length_ratio: double
  child 3, maximum_rejected_chars: int64
  child 4, maximum_train_rows_per_category: int64
  child 5, maximum_validation_rows_per_category: int64
  child 6, minimum_chosen_4gram_diversity: double
  child 7, minimum_chosen_chars: int64
  child 8, minimum_rejected_chars: int64
filtered_candidate_counts: struct<train: int64, validation: int64>
  child 0, train: int64
  child 1, validation: int64
development_categories: struct<economics: int64, education: int64, history: int64, mathematics: int64, moral and ethics: int (... 18 chars omitted)
  child 0, economics: int64
  child 1, education: int64
  child 2, history: int64
  child 3, mathematics: int64
  child 4, moral and ethics: int64
  child 5, sports: int64
seed: int64
source_counts: struct<train: int64, validation: int64>
  child 0, train: int64
  child 1, validation: int64
output_counts: struct<development: int64, final_evaluation: int64, train: int64, validation: int64>
  child 0, development: int64
  child 1, final_evaluation: int64
  child 2, train: int64
  child 3, validation: int64
train_winners: struct<gemma4: int64, qwen3: int64, tigerllm: int64>
  child 0, gemma4: int64
  child 1, qwen3: int64
  child 2, tigerllm: int64
leakage_checks: struct<id_overlap: int64>
  child 0, id_overlap: int64
method: string
validation_categories: struct<economics: int64, education: int64, history: int64, mathematics: int64, moral and ethics: int (... 18 chars omitted)
  child 0, economics: int64
  child 1, education: int64
  child 2, history: int64
  child 3, mathematics: int64
  child 4, moral and ethics: int64
  child 5, sports: int64
to
{'development_categories': {'economics': Value('int64'), 'education': Value('int64'), 'history': Value('int64'), 'mathematics': Value('int64'), 'moral and ethics': Value('int64'), 'sports': Value('int64')}, 'filter': {'development_prompts_per_category': Value('int64'), 'maximum_chosen_chars': Value('int64'), 'maximum_chosen_to_rejected_length_ratio': Value('float64'), 'maximum_rejected_chars': Value('int64'), 'maximum_train_rows_per_category': Value('int64'), 'maximum_validation_rows_per_category': Value('int64'), 'minimum_chosen_4gram_diversity': Value('float64'), 'minimum_chosen_chars': Value('int64'), 'minimum_rejected_chars': Value('int64')}, 'filtered_candidate_counts': {'train': Value('int64'), 'validation': Value('int64')}, 'leakage_checks': {'id_overlap': Value('int64')}, 'method': Value('string'), 'output_counts': {'development': Value('int64'), 'final_evaluation': Value('int64'), 'train': Value('int64'), 'validation': Value('int64')}, 'seed': Value('int64'), 'source_counts': {'train': Value('int64'), 'validation': Value('int64')}, 'train_categories': {'economics': Value('int64'), 'education': Value('int64'), 'history': Value('int64'), 'mathematics': Value('int64'), 'moral and ethics': Value('int64'), 'sports': Value('int64')}, 'train_winners': {'gemma4': Value('int64'), 'qwen3': Value('int64'), 'tigerllm': Value('int64')}, 'validation_categories': {'economics': Value('int64'), 'education': Value('int64'), 'history': Value('int64'), 'mathematics': Value('int64'), 'moral and ethics': Value('int64'), 'sports': Value('int64')}}
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 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 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              category: string
              chosen: list<item: struct<content: string, role: string>>
                child 0, item: struct<content: string, role: string>
                    child 0, content: string
                    child 1, role: string
              duplicate_group_size: int64
              id: string
              kendalls_w: int64
              loser_model: string
              prompt: list<item: struct<content: string, role: string>>
                child 0, item: struct<content: string, role: string>
                    child 0, content: string
                    child 1, role: string
              prompt_text: string
              ranking_status: string
              rejected: list<item: struct<content: string, role: string>>
                child 0, item: struct<content: string, role: string>
                    child 0, content: string
                    child 1, role: string
              source_id: string
              winner_model: string
              train_categories: struct<economics: int64, education: int64, history: int64, mathematics: int64, moral and ethics: int (... 18 chars omitted)
                child 0, economics: int64
                child 1, education: int64
                child 2, history: int64
                child 3, mathematics: int64
                child 4, moral and ethics: int64
                child 5, sports: int64
              filter: struct<development_prompts_per_category: int64, maximum_chosen_chars: int64, maximum_chosen_to_rejec (... 241 chars omitted)
                child 0, development_prompts_per_category: int64
                child 1, maximum_chosen_chars: int64
                child 2, maximum_chosen_to_rejected_length_ratio: double
                child 3, maximum_rejected_chars: int64
                child 4, maximum_train_rows_per_category: int64
                child 5, maximum_validation_rows_per_category: int64
                child 6, minimum_chosen_4gram_diversity: double
                child 7, minimum_chosen_chars: int64
                child 8, minimum_rejected_chars: int64
              filtered_candidate_counts: struct<train: int64, validation: int64>
                child 0, train: int64
                child 1, validation: int64
              development_categories: struct<economics: int64, education: int64, history: int64, mathematics: int64, moral and ethics: int (... 18 chars omitted)
                child 0, economics: int64
                child 1, education: int64
                child 2, history: int64
                child 3, mathematics: int64
                child 4, moral and ethics: int64
                child 5, sports: int64
              seed: int64
              source_counts: struct<train: int64, validation: int64>
                child 0, train: int64
                child 1, validation: int64
              output_counts: struct<development: int64, final_evaluation: int64, train: int64, validation: int64>
                child 0, development: int64
                child 1, final_evaluation: int64
                child 2, train: int64
                child 3, validation: int64
              train_winners: struct<gemma4: int64, qwen3: int64, tigerllm: int64>
                child 0, gemma4: int64
                child 1, qwen3: int64
                child 2, tigerllm: int64
              leakage_checks: struct<id_overlap: int64>
                child 0, id_overlap: int64
              method: string
              validation_categories: struct<economics: int64, education: int64, history: int64, mathematics: int64, moral and ethics: int (... 18 chars omitted)
                child 0, economics: int64
                child 1, education: int64
                child 2, history: int64
                child 3, mathematics: int64
                child 4, moral and ethics: int64
                child 5, sports: int64
              to
              {'development_categories': {'economics': Value('int64'), 'education': Value('int64'), 'history': Value('int64'), 'mathematics': Value('int64'), 'moral and ethics': Value('int64'), 'sports': Value('int64')}, 'filter': {'development_prompts_per_category': Value('int64'), 'maximum_chosen_chars': Value('int64'), 'maximum_chosen_to_rejected_length_ratio': Value('float64'), 'maximum_rejected_chars': Value('int64'), 'maximum_train_rows_per_category': Value('int64'), 'maximum_validation_rows_per_category': Value('int64'), 'minimum_chosen_4gram_diversity': Value('float64'), 'minimum_chosen_chars': Value('int64'), 'minimum_rejected_chars': Value('int64')}, 'filtered_candidate_counts': {'train': Value('int64'), 'validation': Value('int64')}, 'leakage_checks': {'id_overlap': Value('int64')}, 'method': Value('string'), 'output_counts': {'development': Value('int64'), 'final_evaluation': Value('int64'), 'train': Value('int64'), 'validation': Value('int64')}, 'seed': Value('int64'), 'source_counts': {'train': Value('int64'), 'validation': Value('int64')}, 'train_categories': {'economics': Value('int64'), 'education': Value('int64'), 'history': Value('int64'), 'mathematics': Value('int64'), 'moral and ethics': Value('int64'), 'sports': Value('int64')}, 'train_winners': {'gemma4': Value('int64'), 'qwen3': Value('int64'), 'tigerllm': Value('int64')}, 'validation_categories': {'economics': Value('int64'), 'education': Value('int64'), 'history': Value('int64'), 'mathematics': Value('int64'), 'moral and ethics': Value('int64'), 'sports': Value('int64')}}
              because column names don't match