Anthropochrony data
Collection
Tasks, measured runs, SFT sets, estimates, results and logs for Anthropochrony. • 5 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
created_at: string
kept: list<item: string>
child 0, item: string
report: list<item: struct<task_id: string, variant: int64, drafted_tests: int64, dropped_tests: list<item: s (... 130 chars omitted)
child 0, item: struct<task_id: string, variant: int64, drafted_tests: int64, dropped_tests: list<item: string>, ref (... 118 chars omitted)
child 0, task_id: string
child 1, variant: int64
child 2, drafted_tests: int64
child 3, dropped_tests: list<item: string>
child 0, item: string
child 4, reference_flag: string
child 5, reference_times_s: list<item: double>
child 0, item: double
child 6, stub_failure_share: double
child 7, tests: int64
child 8, dropped: string
raw_extra: int64
families: struct<LIB: int64, FUNC: int64, ALGO_IO: int64, ALGO_FN: int64, CLASS: int64>
child 0, LIB: int64
child 1, FUNC: int64
child 2, ALGO_IO: int64
child 3, ALGO_FN: int64
child 4, CLASS: int64
drops: struct<dependency_or_network: int64, language: int64, over_50_public_private_tests: int64, taco_no_p (... 108 chars omitted)
child 0, dependency_or_network: int64
child 1, language: int64
child 2, over_50_public_private_tests: int64
child 3, taco_no_python3_reference: int64
child 4, taco_duplicates_other_source: int64
child 5, reference_failed: int64
child 6, vacuous_tests: int64
splits: struct<train: int64, val: int64, test: int64, test_ood: int64>
child 0, train: int64
child 1, val: int64
child 2, test: int64
child 3, test_ood: int64
to
{'families': {'LIB': Value('int64'), 'FUNC': Value('int64'), 'ALGO_IO': Value('int64'), 'ALGO_FN': Value('int64'), 'CLASS': Value('int64')}, 'splits': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64'), 'test_ood': Value('int64')}, 'raw_extra': Value('int64'), 'drops': {'dependency_or_network': Value('int64'), 'language': Value('int64'), 'over_50_public_private_tests': Value('int64'), 'taco_no_python3_reference': Value('int64'), 'taco_duplicates_other_source': Value('int64'), 'reference_failed': Value('int64'), 'vacuous_tests': Value('int64')}}
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
created_at: string
kept: list<item: string>
child 0, item: string
report: list<item: struct<task_id: string, variant: int64, drafted_tests: int64, dropped_tests: list<item: s (... 130 chars omitted)
child 0, item: struct<task_id: string, variant: int64, drafted_tests: int64, dropped_tests: list<item: string>, ref (... 118 chars omitted)
child 0, task_id: string
child 1, variant: int64
child 2, drafted_tests: int64
child 3, dropped_tests: list<item: string>
child 0, item: string
child 4, reference_flag: string
child 5, reference_times_s: list<item: double>
child 0, item: double
child 6, stub_failure_share: double
child 7, tests: int64
child 8, dropped: string
raw_extra: int64
families: struct<LIB: int64, FUNC: int64, ALGO_IO: int64, ALGO_FN: int64, CLASS: int64>
child 0, LIB: int64
child 1, FUNC: int64
child 2, ALGO_IO: int64
child 3, ALGO_FN: int64
child 4, CLASS: int64
drops: struct<dependency_or_network: int64, language: int64, over_50_public_private_tests: int64, taco_no_p (... 108 chars omitted)
child 0, dependency_or_network: int64
child 1, language: int64
child 2, over_50_public_private_tests: int64
child 3, taco_no_python3_reference: int64
child 4, taco_duplicates_other_source: int64
child 5, reference_failed: int64
child 6, vacuous_tests: int64
splits: struct<train: int64, val: int64, test: int64, test_ood: int64>
child 0, train: int64
child 1, val: int64
child 2, test: int64
child 3, test_ood: int64
to
{'families': {'LIB': Value('int64'), 'FUNC': Value('int64'), 'ALGO_IO': Value('int64'), 'ALGO_FN': Value('int64'), 'CLASS': Value('int64')}, 'splits': {'train': Value('int64'), 'val': Value('int64'), 'test': Value('int64'), 'test_ood': Value('int64')}, 'raw_extra': Value('int64'), 'drops': {'dependency_or_network': Value('int64'), 'language': Value('int64'), 'over_50_public_private_tests': Value('int64'), 'taco_no_python3_reference': Value('int64'), 'taco_duplicates_other_source': Value('int64'), 'reference_failed': Value('int64'), 'vacuous_tests': Value('int64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
anthropochrony-tasks: Task manifest
Part of Anthropochrony: Measuring and Correcting Human-Time Bias in LLM Self-Estimates (Track 1, problem #8). Code, article and reproduction: https://github.com/isHeSatoshi/anthropochrony
Contents. 1,465 Python tasks after CuratorKIT filtering (869 train, 114 validation, 255 test, 93 test_ood unseen family, 134 extra), with source, license, split, near-duplicate group, token counts and ground-truth timing of the reference solution. Also the drop log, the PROJ-8 showcase tasks (our own, Apache-2.0) and rebuild_messages.py.
Restrictions. Rows from LeetCodeDataset, CodeContests and TACO carry IDs and provenance only (restricted_statement: true): no statement, tests, reference code or paraphrase. Rebuild them from the pinned public sources with rebuild_messages.py (see the repository). ClassEval rows are CC BY-NC 4.0.
Licenses. Our annotations, labels and run data: CC-BY-NC-4.0 (they derive from Tiny Aya Global outputs and include CC BY-NC 4.0 ClassEval). Upstream task sources keep their own licenses: HumanEval+/MBPP+ Apache-2.0 (HumanEval MIT, MBPP CC-BY-4.0), BigCodeBench Apache-2.0, ClassEval CC BY-NC 4.0, CodeContests CC-BY-4.0, LeetCodeDataset Apache-2.0 metadata with LeetCode-owned statements, TACO Apache-2.0 metadata with third-party statements. Model-generated text is from CohereLabs/tiny-aya-global (CC-BY-NC-4.0 plus the AUP). Teacher guesses are from DeepSeek-V4-Pro.
Intended use. Research on model self-knowledge and time estimation for coding agents. Not for commercial use.
Limitations. One training seed; labels are medians of 8 sampled runs under stated speeds, not measurements on the stated hardware; Python tasks only; the parser audit covered 60 rows and was done by the research agent.
Collection. https://huggingface.co/collections/isHeSatoshi/anthropochrony-data-6af31e776d6f33265b0c33a8
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