The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label arith-sq256@85d2f4964c72261655837aeca50cb9ce3cc1f164
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2474, in __iter__
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
)
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2391, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2192, in encode_example
return encode_nested_example(self, example)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1472, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1495, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
~~~~~~~~~~~~~~~~~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1168, in encode_example
example_data = self.str2int(example_data)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1105, in str2int
output = [self._strval2int(value) for value in values]
~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1126, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label arith-sq256@85d2f4964c72261655837aeca50cb9ce3cc1f164Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
arith-sq256: square-rendered arithmetic (one-to-one and hard one-to-many targets)
One zip (535 MB, 100k PNGs + jsonl indices). Unzip into your data root; paths inside the jsonl files are relative to the zip root. Used by the code at https://github.com/SuhaoYu1020/Semantic-Image-Release (trained checkpoints: https://huggingface.co/SuhaoYu1020/latent-arith-mmdit).
arithmetic_add_unordered_sq256_same (one-to-one)
Q: a + b = ? rendered on a 256x256 white canvas (DejaVuSansMono-Bold 28 px, margin 8, line spacing 14); the target writes A: <sum> under it.
train 9000 / test 1000 (split by unordered pair {a,b}, 0..99, both orientations rendered). train/inputs, train/targets, test/inputs, test/targets,
train.jsonl / test.jsonl (fields: id, expr, answer, a, b, input, target, ans_box, q_box, digits_box, layout, canvas, font_size), stats.json, _answer_token_box.json (answer box in 16-px tokens).
arithmetic_add_unordered_sq256_same_ansfont_hard_k0 .. k7 (hard one-to-many, 8 valid targets per question)
Same questions and the SAME input images as the base set (inputs not duplicated here: use arithmetic_add_unordered_sq256_same/<split>/inputs/<id>.png).
Target k writes the answer digits in a random one of 8 fonts (DejaVuSansMono-Bold, LiberationMono, DejaVuSansMono-Oblique, Caveat, PermanentMarker,
PressStart2P, Orbitron, Pacifico), rotated uniformly in [-30, 30] deg, shifted by [-10, 10] px horizontally / [-5, 5] px vertically; the draw is seeded by
(question, k) so the 8 variants of a question differ. Each k dir has train/targets, test/targets, train.jsonl, test.jsonl, stats.json.
arithmetic_add_unordered_sq256_same_anshard (index used for training the one-to-many runs)
train.jsonl = 72,000 rows (9000 questions x 8 variants, field variant = k0..k7), test.jsonl = the standard-font test set. Paths are relative to this zip's root.
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