Datasets:

LaneMint
/

lanemint-meta-benchmark

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
total_return: double
final_price: double
problems_found: list<item: string>
  child 0, item: string
annualized_volatility: double
num_clean_days: int64
to
{'annualized_volatility': Value('float64'), 'num_clean_days': Value('int64'), 'problems_found': List(Value('string'))}
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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              total_return: double
              final_price: double
              problems_found: list<item: string>
                child 0, item: string
              annualized_volatility: double
              num_clean_days: int64
              to
              {'annualized_volatility': Value('float64'), 'num_clean_days': Value('int64'), 'problems_found': List(Value('string'))}
              because column names don't match

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LaneMint x Meta Benchmark — public subset

A frozen public subset of the LaneMint x Meta synthetic market-data benchmark: procedurally generated quant-finance data-quality tasks (clean messy vendor price files, then carry out exact numeric work), each with its generated answer key. Built for the Meta AI Developer Hackathon (Startups track, "Push the Frontier").

Status: published. This card is generated by scripts/export_hf_dataset.py (see docs/HUGGINGFACE.md in the source repo for the upload steps). Dataset home: https://huggingface.co/datasets/LaneMint/lanemint-meta-benchmark

data/easy/, data/hard/, data/frontier/ — task CSVs plus tasks.json (the task wording given to a model) per seed.
answers/easy/, answers/hard/, answers/frontier/ — the answer keys generated with these public tasks.
manifest.json — seeds, per-tier/per-seed counts, and SHA-256 of every file.

Seeds and held-out data

Generated from NEW public seeds (default 5001, 5002) that are disjoint from the held-out evaluation seeds (42, 1001, 1002) and from the planned training seeds (2001-2040). No held-out evaluation tasks or answer keys are included. Same seed regenerates the same tasks byte for byte with the source repo's generator.

License

No open-source license has been attached to the source repository yet — all rights are reserved by LaneMint; please ask before reusing the code or data outside this hackathon. All data is procedurally generated synthetic data; there is no real market data.

Regenerate

From the source repository:

python scripts/export_hf_dataset.py

Downloads last month: 66

Total file size:

759 kB

LaneMint x Meta Benchmark — public subset

Contents

Seeds and held-out data

License

Regenerate