Datasets:

---

mandiyaaman

/

nobody-pretraining-shards

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
shard_id: int64
bin_file: string
token_count: int64
num_sequences: int64
max_seq_len: int64
curriculum_stage: string
curriculum_recipe: string
recipe_ratios: struct<cyber_blue: double, cyber_red: double, cyber_purple: double, systems_code: double, formal_log (... 79 chars omitted)
  child 0, cyber_blue: double
  child 1, cyber_red: double
  child 2, cyber_purple: double
  child 3, systems_code: double
  child 4, formal_logic_math: double
  child 5, stem_architecture: double
  child 6, aggregate_cyber_primitives: double
dtype: string
bytes_size: int64
domain_distribution: struct<cyber_purple: int64, cyber_blue: int64, cyber_red: int64, systems_code: int64, formal_logic_m (... 37 chars omitted)
  child 0, cyber_purple: int64
  child 1, cyber_blue: int64
  child 2, cyber_red: int64
  child 3, systems_code: int64
  child 4, formal_logic_math: int64
  child 5, stem_architecture: int64
created_at: timestamp[s]
unique_tokens_sampled: int64
has_thought_or_structural_tags: bool
telemetry: struct<total_evaluated: int64, total_accepted: int64, acceptance_rate_pct: double, rejections_by_cau (... 369 chars omitted)
  child 0, total_evaluated: int64
  child 1, total_accepted: int64
  child 2, acceptance_rate_pct: double
  child 3, rejections_by_cause: struct<low_entropy_boilerplate: int64, low_educational_score: int64, consecutive_ngram_repetition: i (... 264 chars omitted)
      child 0, low_entropy_boilerplate: int64
      child 1, low_educational_score: int64
      child 2, consecutive_ngram_repetition: int64
      child 3, duplicate_lines: int64
      child 4, length_too_long: int64
      child 5, mean_word_len_out_of_range: int64
      child 6, alnum_ratio_out_of_range: int64
      child 7, lacks_mathematical_substance: int64
      child 8, length_too_short: int64
      child 9, non_latin_script: int64
      child 10, boilerplate_detected: int64
      child 11, high_entropy_noise: int64
vocab_coverage_pct: double
compression_entropy_pct: double
shard: string
entropy_status: string
total_tokens: int64
to
{'shard': Value('string'), 'total_tokens': Value('int64'), 'compression_entropy_pct': Value('float64'), 'entropy_status': Value('string'), 'unique_tokens_sampled': Value('int64'), 'vocab_coverage_pct': Value('float64'), 'has_thought_or_structural_tags': Value('bool'), 'telemetry': {'total_evaluated': Value('int64'), 'total_accepted': Value('int64'), 'acceptance_rate_pct': Value('float64'), 'rejections_by_cause': {'low_entropy_boilerplate': Value('int64'), 'low_educational_score': Value('int64'), 'consecutive_ngram_repetition': Value('int64'), 'duplicate_lines': Value('int64'), 'length_too_long': Value('int64'), 'mean_word_len_out_of_range': Value('int64'), 'alnum_ratio_out_of_range': Value('int64'), 'lacks_mathematical_substance': Value('int64'), 'length_too_short': Value('int64'), 'non_latin_script': Value('int64'), 'boilerplate_detected': Value('int64'), 'high_entropy_noise': 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
              shard_id: int64
              bin_file: string
              token_count: int64
              num_sequences: int64
              max_seq_len: int64
              curriculum_stage: string
              curriculum_recipe: string
              recipe_ratios: struct<cyber_blue: double, cyber_red: double, cyber_purple: double, systems_code: double, formal_log (... 79 chars omitted)
                child 0, cyber_blue: double
                child 1, cyber_red: double
                child 2, cyber_purple: double
                child 3, systems_code: double
                child 4, formal_logic_math: double
                child 5, stem_architecture: double
                child 6, aggregate_cyber_primitives: double
              dtype: string
              bytes_size: int64
              domain_distribution: struct<cyber_purple: int64, cyber_blue: int64, cyber_red: int64, systems_code: int64, formal_logic_m (... 37 chars omitted)
                child 0, cyber_purple: int64
                child 1, cyber_blue: int64
                child 2, cyber_red: int64
                child 3, systems_code: int64
                child 4, formal_logic_math: int64
                child 5, stem_architecture: int64
              created_at: timestamp[s]
              unique_tokens_sampled: int64
              has_thought_or_structural_tags: bool
              telemetry: struct<total_evaluated: int64, total_accepted: int64, acceptance_rate_pct: double, rejections_by_cau (... 369 chars omitted)
                child 0, total_evaluated: int64
                child 1, total_accepted: int64
                child 2, acceptance_rate_pct: double
                child 3, rejections_by_cause: struct<low_entropy_boilerplate: int64, low_educational_score: int64, consecutive_ngram_repetition: i (... 264 chars omitted)
                    child 0, low_entropy_boilerplate: int64
                    child 1, low_educational_score: int64
                    child 2, consecutive_ngram_repetition: int64
                    child 3, duplicate_lines: int64
                    child 4, length_too_long: int64
                    child 5, mean_word_len_out_of_range: int64
                    child 6, alnum_ratio_out_of_range: int64
                    child 7, lacks_mathematical_substance: int64
                    child 8, length_too_short: int64
                    child 9, non_latin_script: int64
                    child 10, boilerplate_detected: int64
                    child 11, high_entropy_noise: int64
              vocab_coverage_pct: double
              compression_entropy_pct: double
              shard: string
              entropy_status: string
              total_tokens: int64
              to
              {'shard': Value('string'), 'total_tokens': Value('int64'), 'compression_entropy_pct': Value('float64'), 'entropy_status': Value('string'), 'unique_tokens_sampled': Value('int64'), 'vocab_coverage_pct': Value('float64'), 'has_thought_or_structural_tags': Value('bool'), 'telemetry': {'total_evaluated': Value('int64'), 'total_accepted': Value('int64'), 'acceptance_rate_pct': Value('float64'), 'rejections_by_cause': {'low_entropy_boilerplate': Value('int64'), 'low_educational_score': Value('int64'), 'consecutive_ngram_repetition': Value('int64'), 'duplicate_lines': Value('int64'), 'length_too_long': Value('int64'), 'mean_word_len_out_of_range': Value('int64'), 'alnum_ratio_out_of_range': Value('int64'), 'lacks_mathematical_substance': Value('int64'), 'length_too_short': Value('int64'), 'non_latin_script': Value('int64'), 'boilerplate_detected': Value('int64'), 'high_entropy_noise': Value('int64')}}}
              because column names don't match