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qtris123
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airace-trace-data

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
timestamp: int64
messages: list<item: struct<role: string, content: string>>
  child 0, item: struct<role: string, content: string>
      child 0, role: string
      child 1, content: string
output_length: int64
extra: struct<temperature: int64, seed: int64>
  child 0, temperature: int64
  child 1, seed: int64
texts: list<item: struct<name: string, contents: list<item: string>>>
  child 0, item: struct<name: string, contents: list<item: string>>
      child 0, name: string
      child 1, contents: list<item: string>
          child 0, item: string
to
{'timestamp': Value('int64'), 'texts': List({'name': Value('string'), 'contents': List(Value('string'))}), 'output_length': Value('int64'), 'extra': {'temperature': Value('int64'), 'seed': Value('int64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              timestamp: int64
              messages: list<item: struct<role: string, content: string>>
                child 0, item: struct<role: string, content: string>
                    child 0, role: string
                    child 1, content: string
              output_length: int64
              extra: struct<temperature: int64, seed: int64>
                child 0, temperature: int64
                child 1, seed: int64
              texts: list<item: struct<name: string, contents: list<item: string>>>
                child 0, item: struct<name: string, contents: list<item: string>>
                    child 0, name: string
                    child 1, contents: list<item: string>
                        child 0, item: string
              to
              {'timestamp': Value('int64'), 'texts': List({'name': Value('string'), 'contents': List(Value('string'))}), 'output_length': Value('int64'), 'extra': {'temperature': Value('int64'), 'seed': Value('int64')}}
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, 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

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timestamp
int64
texts
list
output_length
int64
extra
dict
0
[ { "name": "system", "contents": [ "node implementation capacity at batch stream rank, network effective user score data load compute, output under system under recommendation with. Check, robust gradient queue previous scalable plan context, assistant with analysis or example section metric, driver wi...
200
{ "temperature": 0, "seed": 42 }
25
[ { "name": "system", "contents": [ "node implementation capacity at batch stream rank, network effective user score data load compute, output under system under recommendation with. Check, robust gradient queue previous scalable plan context, assistant with analysis or example section metric, driver wi...
200
{ "temperature": 0, "seed": 42 }
50
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
75
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
100
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
125
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
150
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
175
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
200
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
225
[{"name":"system","contents":["node implementation capacity at batch stream rank, network effective (...TRUNCATED)
200
{ "temperature": 0, "seed": 42 }
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