Datasets:

---

ashish-soni08

/

repro-expert-sample-results

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model: string
n_problems: int64
n_samples: int64
max_new_tokens: int64
conditions: struct<baseline_T0.7: struct<temperature: double, expert_sample: bool, mean_distinct_answer_ratio: d (... 295 chars omitted)
  child 0, baseline_T0.7: struct<temperature: double, expert_sample: bool, mean_distinct_answer_ratio: double, mean_distinct3_ (... 75 chars omitted)
      child 0, temperature: double
      child 1, expert_sample: bool
      child 2, mean_distinct_answer_ratio: double
      child 3, mean_distinct3_ngram_ratio: double
      child 4, mean_max_repetition_rate: double
      child 5, mean_tokens: double
  child 1, expert_sample_T0.7: struct<temperature: double, expert_sample: bool, mean_distinct_answer_ratio: double, mean_distinct3_ (... 75 chars omitted)
      child 0, temperature: double
      child 1, expert_sample: bool
      child 2, mean_distinct_answer_ratio: double
      child 3, mean_distinct3_ngram_ratio: double
      child 4, mean_max_repetition_rate: double
      child 5, mean_tokens: double
es_hparams: struct<k_keep: int64, tau: double, r: int64>
  child 0, k_keep: int64
  child 1, tau: double
  child 2, r: int64
wall_seconds: double
claim_tail_cumdiff_lt_1.5pct: bool
n_token_layer_observations: int64
n_prompts: int64
rank_mean_scores_top32: list<item: double>
  child 0, item: double
claim_head_gap_gt_3.9pct: bool
head_gap_rank1_to_rank5: double
num_experts: int64
rank1: double
rank32: double
full_mean_128: list<item: double>
  child 0, item: double
top_k: int64
tail_cumdiff_rank5_to_rank32: double
rank5: double
to
{'model': Value('string'), 'num_experts': Value('int64'), 'top_k': Value('int64'), 'n_prompts': Value('int64'), 'max_new_tokens': Value('int64'), 'n_token_layer_observations': Value('int64'), 'rank_mean_scores_top32': List(Value('float64')), 'rank1': Value('float64'), 'rank5': Value('float64'), 'rank32': Value('float64'), 'head_gap_rank1_to_rank5': Value('float64'), 'tail_cumdiff_rank5_to_rank32': Value('float64'), 'claim_head_gap_gt_3.9pct': Value('bool'), 'claim_tail_cumdiff_lt_1.5pct': Value('bool'), 'wall_seconds': Value('float64'), 'full_mean_128': List(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 489, 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
              model: string
              n_problems: int64
              n_samples: int64
              max_new_tokens: int64
              conditions: struct<baseline_T0.7: struct<temperature: double, expert_sample: bool, mean_distinct_answer_ratio: d (... 295 chars omitted)
                child 0, baseline_T0.7: struct<temperature: double, expert_sample: bool, mean_distinct_answer_ratio: double, mean_distinct3_ (... 75 chars omitted)
                    child 0, temperature: double
                    child 1, expert_sample: bool
                    child 2, mean_distinct_answer_ratio: double
                    child 3, mean_distinct3_ngram_ratio: double
                    child 4, mean_max_repetition_rate: double
                    child 5, mean_tokens: double
                child 1, expert_sample_T0.7: struct<temperature: double, expert_sample: bool, mean_distinct_answer_ratio: double, mean_distinct3_ (... 75 chars omitted)
                    child 0, temperature: double
                    child 1, expert_sample: bool
                    child 2, mean_distinct_answer_ratio: double
                    child 3, mean_distinct3_ngram_ratio: double
                    child 4, mean_max_repetition_rate: double
                    child 5, mean_tokens: double
              es_hparams: struct<k_keep: int64, tau: double, r: int64>
                child 0, k_keep: int64
                child 1, tau: double
                child 2, r: int64
              wall_seconds: double
              claim_tail_cumdiff_lt_1.5pct: bool
              n_token_layer_observations: int64
              n_prompts: int64
              rank_mean_scores_top32: list<item: double>
                child 0, item: double
              claim_head_gap_gt_3.9pct: bool
              head_gap_rank1_to_rank5: double
              num_experts: int64
              rank1: double
              rank32: double
              full_mean_128: list<item: double>
                child 0, item: double
              top_k: int64
              tail_cumdiff_rank5_to_rank32: double
              rank5: double
              to
              {'model': Value('string'), 'num_experts': Value('int64'), 'top_k': Value('int64'), 'n_prompts': Value('int64'), 'max_new_tokens': Value('int64'), 'n_token_layer_observations': Value('int64'), 'rank_mean_scores_top32': List(Value('float64')), 'rank1': Value('float64'), 'rank5': Value('float64'), 'rank32': Value('float64'), 'head_gap_rank1_to_rank5': Value('float64'), 'tail_cumdiff_rank5_to_rank32': Value('float64'), 'claim_head_gap_gt_3.9pct': Value('bool'), 'claim_tail_cumdiff_lt_1.5pct': Value('bool'), 'wall_seconds': Value('float64'), 'full_mean_128': List(Value('float64'))}
              because column names don't match