Datasets:

---

bhoy

/

es-vpo-awd-checkpoints

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
iteration: int64
reward_weight: double
best_accepted_reward: double
plateau_count: int64
accepted_reward_history: list<item: double>
  child 0, item: double
last_adapt_info: struct<adapt_rule: string, accepted_reward: double, min_delta: double, window: int64, enough_history (... 153 chars omitted)
  child 0, adapt_rule: string
  child 1, accepted_reward: double
  child 2, min_delta: double
  child 3, window: int64
  child 4, enough_history: bool
  child 5, recent_mean: double
  child 6, previous_mean: double
  child 7, mean_delta: double
  child 8, improved: bool
  child 9, plateau_count: int64
  child 10, reward_weight: double
  child 11, history_size: int64
archive: list<item: list<item: double>>
  child 0, item: list<item: double>
      child 0, item: double
last_accepted_reward: double
last_accepted_behavior: list<item: double>
  child 0, item: double
data_path: string
best@10: double
pool_size: double
current_best@10: double
f1_at_best@30: double
unique_vector_count: double
best_f1@30: double
best@30: double
seed: int64
temperature: double
max_token_hits: int64
candidate_count: double
best@5: double
current_best@3: double
num_prompts: int64
top_p: double
pool_vector_l1: double
checkpoint: string
model_name: string
current_best@30: double
best@3: double
num_completions: int64
mean_completion_reward: double
unique_vector_ratio: double
best_completion_reward: double
elapsed_seconds: double
mean_internal_vector_l1: double
current_best@5: double
mean_output_tokens: double
best_answer_reward: double
max_tokens: int64
best_support_reward: double
finish_reasons: struct<stop: int64, length: int64>
  child 0, stop: int64
  child 1, length: int64
to
{'model_name': Value('string'), 'checkpoint': Value('string'), 'data_path': Value('string'), 'num_prompts': Value('int64'), 'num_completions': Value('int64'), 'temperature': Value('float64'), 'top_p': Value('float64'), 'max_tokens': Value('int64'), 'seed': Value('int64'), 'pool_vector_l1': Value('float64'), 'mean_internal_vector_l1': Value('float64'), 'best@3': Value('float64'), 'best@5': Value('float64'), 'best@10': Value('float64'), 'best@30': Value('float64'), 'f1_at_best@30': Value('float64'), 'best_f1@30': Value('float64'), 'current_best@3': Value('float64'), 'current_best@5': Value('float64'), 'current_best@10': Value('float64'), 'current_best@30': Value('float64'), 'unique_vector_count': Value('float64'), 'unique_vector_ratio': Value('float64'), 'pool_size': Value('float64'), 'candidate_count': Value('float64'), 'mean_completion_reward': Value('float64'), 'best_completion_reward': Value('float64'), 'best_answer_reward': Value('float64'), 'best_support_reward': Value('float64'), 'mean_output_tokens': Value('float64'), 'max_token_hits': Value('int64'), 'finish_reasons': {'stop': Value('int64'), 'length': Value('int64')}, 'elapsed_seconds': Value('float64')}
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 478, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              iteration: int64
              reward_weight: double
              best_accepted_reward: double
              plateau_count: int64
              accepted_reward_history: list<item: double>
                child 0, item: double
              last_adapt_info: struct<adapt_rule: string, accepted_reward: double, min_delta: double, window: int64, enough_history (... 153 chars omitted)
                child 0, adapt_rule: string
                child 1, accepted_reward: double
                child 2, min_delta: double
                child 3, window: int64
                child 4, enough_history: bool
                child 5, recent_mean: double
                child 6, previous_mean: double
                child 7, mean_delta: double
                child 8, improved: bool
                child 9, plateau_count: int64
                child 10, reward_weight: double
                child 11, history_size: int64
              archive: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              last_accepted_reward: double
              last_accepted_behavior: list<item: double>
                child 0, item: double
              data_path: string
              best@10: double
              pool_size: double
              current_best@10: double
              f1_at_best@30: double
              unique_vector_count: double
              best_f1@30: double
              best@30: double
              seed: int64
              temperature: double
              max_token_hits: int64
              candidate_count: double
              best@5: double
              current_best@3: double
              num_prompts: int64
              top_p: double
              pool_vector_l1: double
              checkpoint: string
              model_name: string
              current_best@30: double
              best@3: double
              num_completions: int64
              mean_completion_reward: double
              unique_vector_ratio: double
              best_completion_reward: double
              elapsed_seconds: double
              mean_internal_vector_l1: double
              current_best@5: double
              mean_output_tokens: double
              best_answer_reward: double
              max_tokens: int64
              best_support_reward: double
              finish_reasons: struct<stop: int64, length: int64>
                child 0, stop: int64
                child 1, length: int64
              to
              {'model_name': Value('string'), 'checkpoint': Value('string'), 'data_path': Value('string'), 'num_prompts': Value('int64'), 'num_completions': Value('int64'), 'temperature': Value('float64'), 'top_p': Value('float64'), 'max_tokens': Value('int64'), 'seed': Value('int64'), 'pool_vector_l1': Value('float64'), 'mean_internal_vector_l1': Value('float64'), 'best@3': Value('float64'), 'best@5': Value('float64'), 'best@10': Value('float64'), 'best@30': Value('float64'), 'f1_at_best@30': Value('float64'), 'best_f1@30': Value('float64'), 'current_best@3': Value('float64'), 'current_best@5': Value('float64'), 'current_best@10': Value('float64'), 'current_best@30': Value('float64'), 'unique_vector_count': Value('float64'), 'unique_vector_ratio': Value('float64'), 'pool_size': Value('float64'), 'candidate_count': Value('float64'), 'mean_completion_reward': Value('float64'), 'best_completion_reward': Value('float64'), 'best_answer_reward': Value('float64'), 'best_support_reward': Value('float64'), 'mean_output_tokens': Value('float64'), 'max_token_hits': Value('int64'), 'finish_reasons': {'stop': Value('int64'), 'length': Value('int64')}, 'elapsed_seconds': Value('float64')}
              because column names don't match