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: TypeError
Message: Couldn't cast array of type double to null
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 2315, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2121, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2071, in cast_array_to_feature
_c(children[name] if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2071, in cast_array_to_feature
_c(children[name] if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2121, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2155, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type double to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
GeoSym-Bench
A controlled benchmark of synthetic function charts with exact algebraic ground truth, released with GeoSym: Explicit Evidence Construction and Layered Reasoning for Quantitative Chart Understanding (code).
Contents
- Pool: 480 charts, 60 per function family, 1440 x 1440 PNG, each with one ground-truth JSON.
- Evaluated roster:
roster_240.jsonlists the 240 samples behind the reported results, with per-family counts (19-32) and SHA-256 checksums for every image and ground-truth file. The sample list is authoritative. Report per-family results with their n.
These files are the canonical benchmark. The generator in the code repository does not reproduce them byte for byte, so verify downloads against roster_240.json.
| Category | Family | General form | Pool | Roster |
|---|---|---|---|---|
| Elementary | linear | y = ax + b | 60 | 19 |
| Elementary | quadratic | y = a(x-h)^2 + k | 60 | 31 |
| Elementary | absolute | y = a|x-h| + k | 60 | 32 |
| Polynomial | cubic | y = a(x-h)^3 + b(x-h) + k | 60 | 32 |
| Piecewise | piecewise_linear | two affine branches joined at (h, k) | 60 | 31 |
| Transcendental | exponential | y = a * b^(x-h) + k | 60 | 32 |
| Transcendental | logarithmic | y = a * log_2(x-h) + k | 60 | 32 |
| Special | rational | y = a/(x-h) + k | 60 | 31 |
Evaluation dimensions
Ten dimensions: intercepts, extrema, predictions, rate of change, monotonicity, symmetry, expression, domain, range and inference quality. D1-D9 are scored against exact ground truth. D10 is scored by a judge model that differs from the reasoning model. The dimensions are not equally weighted: they are combined by the three-group weighting in the code repository's config/default_config.yaml (reasoning.layers), with effective weights from 0.045 to 0.150.
Structure
images/{family}-clean_render/{family}-clean_render_NNNN.png
ground_truth/{family}-clean_render/{family}-clean_render_NNNN.json
roster_240.json
Each ground-truth JSON holds rendering metadata (chart_bbox, page_size, layout), axis specifications, exact curve-grid intersections, function parameters and expression, the answers for all ten dimensions (question.ground_truth), and benchmark_meta.
Usage
from huggingface_hub import snapshot_download
path = snapshot_download(repo_id="lihaonan0716/GeoSym-Bench", repo_type="dataset")
License
Citation
@misc{wang2026geosym,
title = {GeoSym: Explicit Evidence Construction and Layered Reasoning for Quantitative Chart Understanding},
author = {Wang, Yongqing and Zhang, Qisheng and Guo, Linyan and Li, Haonan and Sun, Tianjun and Yu, Samson S.},
year = {2026},
note = {Manuscript under review},
url = {https://github.com/lihaonan0716/GeoSym}
}
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