Datasets:

---

LongDuy

/

markllm-clean-benchmark

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
sample_id: int64
dataset: string
prompt: string
generation_seed: int64
reason: string
budget_seconds: double
generation_time_sec: double
sentence_trials: list<item: int64>
  child 0, item: int64
forced_acceptances: int64
fingerprint: string
runner_sha256: string
centroid_sha256: string
sampling: struct<max_new_tokens: int64, min_new_tokens: int64, do_sample: bool, temperature: double, top_p: do (... 96 chars omitted)
  child 0, max_new_tokens: int64
  child 1, min_new_tokens: int64
  child 2, do_sample: bool
  child 3, temperature: double
  child 4, top_p: double
  child 5, top_k: int64
  child 6, repetition_penalty: double
  child 7, no_repeat_ngram_size: int64
  child 8, eos_token_id: null
input_sha256: string
model: string
runtime_policy: struct<baseline_seconds: int64, timeout_floor: int64, timeout_multiplier: int64, timeout_ceiling: in (... 4 chars omitted)
  child 0, baseline_seconds: int64
  child 1, timeout_floor: int64
  child 2, timeout_multiplier: int64
  child 3, timeout_ceiling: int64
seed_rule: string
embedder: string
revision: string
embedder_revision: string
watermark: struct<algorithm_name: string, domain_data: string, max_new_tokens: int64, min_new_tokens: int64, N_ (... 124 chars omitted)
  child 0, algorithm_name: string
  child 1, domain_data: string
  child 2, max_new_tokens: int64
  child 3, min_new_tokens: int64
  child 4, N_max: int64
  child 5, gamma: double
  child 6, margin_m: double
  child 7, k: int64
  child 8, prime_P: int64
  child 9, threshold: double
  child 10, target_continuation_tokens: int64
implementation_sha256: string
to
{'model': Value('string'), 'revision': Value('string'), 'dataset': Value('string'), 'embedder': Value('string'), 'embedder_revision': Value('string'), 'centroid_sha256': Value('string'), 'sampling': {'max_new_tokens': Value('int64'), 'min_new_tokens': Value('int64'), 'do_sample': Value('bool'), 'temperature': Value('float64'), 'top_p': Value('float64'), 'top_k': Value('int64'), 'repetition_penalty': Value('float64'), 'no_repeat_ngram_size': Value('int64'), 'eos_token_id': Value('null')}, 'watermark': {'algorithm_name': Value('string'), 'domain_data': Value('string'), 'max_new_tokens': Value('int64'), 'min_new_tokens': Value('int64'), 'N_max': Value('int64'), 'gamma': Value('float64'), 'margin_m': Value('float64'), 'k': Value('int64'), 'prime_P': Value('int64'), 'threshold': Value('float64'), 'target_continuation_tokens': Value('int64')}, 'input_sha256': Value('string'), 'seed_rule': Value('string'), 'implementation_sha256': Value('string'), 'runner_sha256': Value('string'), 'runtime_policy': {'baseline_seconds': Value('int64'), 'timeout_floor': Value('int64'), 'timeout_multiplier': Value('int64'), 'timeout_ceiling': 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
              sample_id: int64
              dataset: string
              prompt: string
              generation_seed: int64
              reason: string
              budget_seconds: double
              generation_time_sec: double
              sentence_trials: list<item: int64>
                child 0, item: int64
              forced_acceptances: int64
              fingerprint: string
              runner_sha256: string
              centroid_sha256: string
              sampling: struct<max_new_tokens: int64, min_new_tokens: int64, do_sample: bool, temperature: double, top_p: do (... 96 chars omitted)
                child 0, max_new_tokens: int64
                child 1, min_new_tokens: int64
                child 2, do_sample: bool
                child 3, temperature: double
                child 4, top_p: double
                child 5, top_k: int64
                child 6, repetition_penalty: double
                child 7, no_repeat_ngram_size: int64
                child 8, eos_token_id: null
              input_sha256: string
              model: string
              runtime_policy: struct<baseline_seconds: int64, timeout_floor: int64, timeout_multiplier: int64, timeout_ceiling: in (... 4 chars omitted)
                child 0, baseline_seconds: int64
                child 1, timeout_floor: int64
                child 2, timeout_multiplier: int64
                child 3, timeout_ceiling: int64
              seed_rule: string
              embedder: string
              revision: string
              embedder_revision: string
              watermark: struct<algorithm_name: string, domain_data: string, max_new_tokens: int64, min_new_tokens: int64, N_ (... 124 chars omitted)
                child 0, algorithm_name: string
                child 1, domain_data: string
                child 2, max_new_tokens: int64
                child 3, min_new_tokens: int64
                child 4, N_max: int64
                child 5, gamma: double
                child 6, margin_m: double
                child 7, k: int64
                child 8, prime_P: int64
                child 9, threshold: double
                child 10, target_continuation_tokens: int64
              implementation_sha256: string
              to
              {'model': Value('string'), 'revision': Value('string'), 'dataset': Value('string'), 'embedder': Value('string'), 'embedder_revision': Value('string'), 'centroid_sha256': Value('string'), 'sampling': {'max_new_tokens': Value('int64'), 'min_new_tokens': Value('int64'), 'do_sample': Value('bool'), 'temperature': Value('float64'), 'top_p': Value('float64'), 'top_k': Value('int64'), 'repetition_penalty': Value('float64'), 'no_repeat_ngram_size': Value('int64'), 'eos_token_id': Value('null')}, 'watermark': {'algorithm_name': Value('string'), 'domain_data': Value('string'), 'max_new_tokens': Value('int64'), 'min_new_tokens': Value('int64'), 'N_max': Value('int64'), 'gamma': Value('float64'), 'margin_m': Value('float64'), 'k': Value('int64'), 'prime_P': Value('int64'), 'threshold': Value('float64'), 'target_continuation_tokens': Value('int64')}, 'input_sha256': Value('string'), 'seed_rule': Value('string'), 'implementation_sha256': Value('string'), 'runner_sha256': Value('string'), 'runtime_policy': {'baseline_seconds': Value('int64'), 'timeout_floor': Value('int64'), 'timeout_multiplier': Value('int64'), 'timeout_ceiling': Value('int64')}}
              because column names don't match