Datasets:

---

LevArtesa

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sft-humanizer-dataset-v5

like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
actual_count: int64
below_minimum: bool
built_at: string
cumulative_gptzero_cost_usd: double
cumulative_total_cost_usd: double
minimum_target: int64
n_train: int64
n_val: int64
network_sources_skipped: struct<coral-nlp:scientific: bool, v51-fresh: bool>
  child 0, coral-nlp:scientific: bool
  child 1, v51-fresh: bool
output_path: string
pool_size_after_dedupe: int64
rebalance_low_keep: int64
rebalance_note: string
rebalance_seed: int64
rebalance_var_y: double
rebalanced: bool
resumed: bool
schema_version: int64
score_cache_hit_rate: double
score_cache_hits: int64
scored: bool
seed: int64
selected_source_counts: struct<finetune-sft-v5:human: int64, v4-backtrans-filtered: int64, v4-stage-0-baseline: int64>
  child 0, finetune-sft-v5:human: int64
  child 1, v4-backtrans-filtered: int64
  child 2, v4-stage-0-baseline: int64
source_counts: struct<coral-nlp:scientific: int64, finetune-sft-v5:human: int64, v4-backtrans-filtered: int64, v4-s (... 41 chars omitted)
  child 0, coral-nlp:scientific: int64
  child 1, finetune-sft-v5:human: int64
  child 2, v4-backtrans-filtered: int64
  child 3, v4-stage-0-baseline: int64
  child 4, v51-fresh: int64
target_count: int64
source: string
scored_at: string
text: string
ai_prob: double
to
{'ai_prob': Value('float64'), 'scored_at': Value('string'), 'source': Value('string'), 'text': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              actual_count: int64
              below_minimum: bool
              built_at: string
              cumulative_gptzero_cost_usd: double
              cumulative_total_cost_usd: double
              minimum_target: int64
              n_train: int64
              n_val: int64
              network_sources_skipped: struct<coral-nlp:scientific: bool, v51-fresh: bool>
                child 0, coral-nlp:scientific: bool
                child 1, v51-fresh: bool
              output_path: string
              pool_size_after_dedupe: int64
              rebalance_low_keep: int64
              rebalance_note: string
              rebalance_seed: int64
              rebalance_var_y: double
              rebalanced: bool
              resumed: bool
              schema_version: int64
              score_cache_hit_rate: double
              score_cache_hits: int64
              scored: bool
              seed: int64
              selected_source_counts: struct<finetune-sft-v5:human: int64, v4-backtrans-filtered: int64, v4-stage-0-baseline: int64>
                child 0, finetune-sft-v5:human: int64
                child 1, v4-backtrans-filtered: int64
                child 2, v4-stage-0-baseline: int64
              source_counts: struct<coral-nlp:scientific: int64, finetune-sft-v5:human: int64, v4-backtrans-filtered: int64, v4-s (... 41 chars omitted)
                child 0, coral-nlp:scientific: int64
                child 1, finetune-sft-v5:human: int64
                child 2, v4-backtrans-filtered: int64
                child 3, v4-stage-0-baseline: int64
                child 4, v51-fresh: int64
              target_count: int64
              source: string
              scored_at: string
              text: string
              ai_prob: double
              to
              {'ai_prob': Value('float64'), 'scored_at': Value('string'), 'source': Value('string'), 'text': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1348, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset