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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1531, in _prepare_split_single
for key, record 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/webdataset/webdataset.py", line 127, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1400, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 977, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 786, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
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 1393, 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 1571, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
npz dict | __key__ string | __url__ string |
|---|---|---|
{"sample_ids":[9205528027379138574,6782524250851639373,17470784268884312340,1648840374452563007,1732(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r01_center_3_5 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[13233992183485940008,17703265332465579981,11685650837051756798,14790374564328781129,1(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r01_center_5_3 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[133437114357403041,5115753334975421344,6638267282207556950,14431252769649747882,82201(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r01_center_5_7 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[3594458230517950955,9153422254947601767,4797210763435489035,6086128109605269277,96268(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r01_center_7_5 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[17842616375560617470,5507110209294886314,8341516350269429190,10675612909277914712,882(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r03_center_3_5 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[17423611485441114909,11247925951430791453,1104171840872580476,17614718714458066903,84(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r03_center_5_3 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[17908321738343235347,6290085622577429941,5272211586515656586,5629200562140189933,1138(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r03_center_5_7 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[9456971523240946570,7592128579900114606,13164334374434383862,838558027966286266,67109(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r03_center_7_5 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[545386992424719375,12186822269514006707,16510209880105246204,3034816877002224386,4741(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r05_center_3_5 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
{"sample_ids":[10419480523160465963,2036594104165535533,4259859363802340811,6725652424740120753,1561(...TRUNCATED) | grader/conditions/surface_code_bX_d3_r05_center_5_3 | "hf://datasets/sunweiwei/ai4sci-surface-code-decoding-evaluation@d1985d6041a00491b3fca9254f44457a5ca(...TRUNCATED) |
End of preview.
Trusted evaluator distribution
This repository contains grader-labels.tar.gz, a private operator distribution
for the public materialized benchmark,
and reference-model.tar.gz, the frozen reference AlphaQubit submission operators
replay as a known answer (P=0.75506 on the full test). Neither is ever mounted for agents.
It holds official-path binary logical-flip labels and opaque sample IDs for
3,250,000 odd-indexed Sycamore shots across 130 fixed conditions. Its manifest
binds labels to the exact input-only verifier manifest via SHA-256.
Only the trusted grader may mount the extracted grader/ directory. The
prediction container and development agent must never receive it. The grader
never imports submitted model source or executes submitted code. This private
packaging controls runtime access; the upstream historical data is public.
Original source: Google Quantum AI, Zenodo 6804040, CC-BY-4.0. Attribute Google Quantum AI and retain the source citation when redistributing derived data. The fixed score configuration uses same-test MWPM/TN anchors, preserves raw performance, and clips normalized score to [0,1]. Use the immutable revision and archive SHA-256 pinned in the GitHub task.
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