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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 11 new columns ({'pred_raw', 'pred_norm', 'truncated', 'passage_or_group', 'gold', 'sample_id', 'task', 'correct_norm', 'scores_ll_4cand', 'model', 'correct_raw'}) and 6 missing columns ({'acc', 'acc_pct', 'n', 'ci_hi', 'ci_lo', 'subject'}).
This happened while the csv dataset builder was generating data using
hf://datasets/umeiko/haidass-eval/base_capability_eval/predictions_all.csv (at revision d0f9d7553f19707ed4038e44f5940acf18948b46), ['hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_cagliostro_v3.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_gptx25_135m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_haidass143m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_mobilellm_r1_140m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_qwen25_05b.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_qwen3_06b.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_smollm2_135m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/predictions_all.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/slm_summary.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/summary.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/summary_accnorm.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/summary_ci.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
task: string
sample_id: string
passage_or_group: string
gold: string
scores_ll_4cand: string
pred_norm: string
correct_norm: int64
pred_raw: string
correct_raw: int64
truncated: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1576
to
{'subject': Value('string'), 'n': Value('int64'), 'acc': Value('float64'), 'acc_pct': Value('float64'), 'ci_lo': Value('float64'), 'ci_hi': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
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 1694, 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 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 11 new columns ({'pred_raw', 'pred_norm', 'truncated', 'passage_or_group', 'gold', 'sample_id', 'task', 'correct_norm', 'scores_ll_4cand', 'model', 'correct_raw'}) and 6 missing columns ({'acc', 'acc_pct', 'n', 'ci_hi', 'ci_lo', 'subject'}).
This happened while the csv dataset builder was generating data using
hf://datasets/umeiko/haidass-eval/base_capability_eval/predictions_all.csv (at revision d0f9d7553f19707ed4038e44f5940acf18948b46), ['hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_cagliostro_v3.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_gptx25_135m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_haidass143m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_mobilellm_r1_140m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_qwen25_05b.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_qwen3_06b.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/cmmlu_subjects_smollm2_135m.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/predictions_all.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/slm_summary.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/summary.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/summary_accnorm.csv', 'hf://datasets/umeiko/haidass-eval@d0f9d7553f19707ed4038e44f5940acf18948b46/base_capability_eval/summary_ci.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
subject string | n int64 | acc float64 | acc_pct float64 | ci_lo float64 | ci_hi float64 |
|---|---|---|---|---|---|
agronomy | 169 | 0.242604 | 24.26 | 0.1775 | 0.3077 |
anatomy | 148 | 0.256757 | 25.68 | 0.1892 | 0.3378 |
ancient_chinese | 164 | 0.256098 | 25.61 | 0.1951 | 0.3232 |
arts | 160 | 0.25625 | 25.62 | 0.1938 | 0.3312 |
astronomy | 165 | 0.248485 | 24.85 | 0.1818 | 0.3152 |
business_ethics | 209 | 0.248804 | 24.88 | 0.1914 | 0.311 |
chinese_civil_service_exam | 160 | 0.25625 | 25.62 | 0.1938 | 0.3187 |
chinese_driving_rule | 131 | 0.251908 | 25.19 | 0.1756 | 0.3282 |
chinese_food_culture | 136 | 0.25 | 25 | 0.1838 | 0.3309 |
chinese_foreign_policy | 107 | 0.252336 | 25.23 | 0.1682 | 0.3364 |
chinese_history | 323 | 0.250774 | 25.08 | 0.2043 | 0.3003 |
chinese_literature | 204 | 0.254902 | 25.49 | 0.1961 | 0.3137 |
chinese_teacher_qualification | 179 | 0.251397 | 25.14 | 0.1899 | 0.3184 |
clinical_knowledge | 237 | 0.253165 | 25.32 | 0.1983 | 0.308 |
college_actuarial_science | 106 | 0.245283 | 24.53 | 0.1792 | 0.3302 |
college_education | 107 | 0.317757 | 31.78 | 0.2336 | 0.4019 |
college_engineering_hydrology | 106 | 0.301887 | 30.19 | 0.217 | 0.3868 |
college_law | 108 | 0.212963 | 21.3 | 0.1481 | 0.2963 |
college_mathematics | 105 | 0.219048 | 21.9 | 0.1429 | 0.2952 |
college_medical_statistics | 106 | 0.254717 | 25.47 | 0.1792 | 0.3396 |
college_medicine | 273 | 0.241758 | 24.18 | 0.1941 | 0.293 |
computer_science | 204 | 0.254902 | 25.49 | 0.1961 | 0.3186 |
computer_security | 171 | 0.25731 | 25.73 | 0.193 | 0.3216 |
conceptual_physics | 147 | 0.251701 | 25.17 | 0.1837 | 0.3265 |
construction_project_management | 139 | 0.244604 | 24.46 | 0.1799 | 0.3165 |
economics | 159 | 0.245283 | 24.53 | 0.1761 | 0.3145 |
education | 163 | 0.251534 | 25.15 | 0.184 | 0.319 |
electrical_engineering | 172 | 0.25 | 25 | 0.186 | 0.314 |
elementary_chinese | 252 | 0.281746 | 28.17 | 0.2262 | 0.3413 |
elementary_commonsense | 198 | 0.242424 | 24.24 | 0.1869 | 0.303 |
elementary_information_and_technology | 238 | 0.268908 | 26.89 | 0.2101 | 0.3277 |
elementary_mathematics | 230 | 0.278261 | 27.83 | 0.2217 | 0.3348 |
ethnology | 135 | 0.251852 | 25.19 | 0.1778 | 0.3259 |
food_science | 143 | 0.251748 | 25.17 | 0.1818 | 0.3217 |
genetics | 176 | 0.244318 | 24.43 | 0.1818 | 0.3125 |
global_facts | 149 | 0.248322 | 24.83 | 0.1812 | 0.3221 |
high_school_biology | 169 | 0.248521 | 24.85 | 0.1834 | 0.3136 |
high_school_chemistry | 132 | 0.257576 | 25.76 | 0.1818 | 0.3333 |
high_school_geography | 118 | 0.254237 | 25.42 | 0.178 | 0.339 |
high_school_mathematics | 164 | 0.25 | 25 | 0.1829 | 0.3171 |
high_school_physics | 110 | 0.254545 | 25.45 | 0.1727 | 0.3455 |
high_school_politics | 143 | 0.251748 | 25.17 | 0.1818 | 0.3287 |
human_sexuality | 126 | 0.253968 | 25.4 | 0.1825 | 0.3333 |
international_law | 185 | 0.248649 | 24.86 | 0.1892 | 0.3135 |
journalism | 172 | 0.25 | 25 | 0.186 | 0.314 |
jurisprudence | 411 | 0.250608 | 25.06 | 0.2117 | 0.292 |
legal_and_moral_basis | 214 | 0.252336 | 25.23 | 0.1963 | 0.3084 |
logical | 123 | 0.252033 | 25.2 | 0.1789 | 0.3333 |
machine_learning | 122 | 0.254098 | 25.41 | 0.1721 | 0.3279 |
management | 210 | 0.247619 | 24.76 | 0.1952 | 0.3048 |
marketing | 180 | 0.25 | 25 | 0.1944 | 0.3167 |
marxist_theory | 189 | 0.248677 | 24.87 | 0.1905 | 0.3122 |
modern_chinese | 116 | 0.25 | 25 | 0.1724 | 0.3362 |
nutrition | 145 | 0.255172 | 25.52 | 0.1862 | 0.3241 |
philosophy | 105 | 0.247619 | 24.76 | 0.1619 | 0.3333 |
professional_accounting | 175 | 0.251429 | 25.14 | 0.1943 | 0.32 |
professional_law | 211 | 0.251185 | 25.12 | 0.1896 | 0.3081 |
professional_medicine | 376 | 0.25 | 25 | 0.2048 | 0.2979 |
professional_psychology | 232 | 0.25 | 25 | 0.1983 | 0.3017 |
public_relations | 174 | 0.252874 | 25.29 | 0.1954 | 0.3161 |
security_study | 135 | 0.251852 | 25.19 | 0.1778 | 0.3259 |
sociology | 226 | 0.252212 | 25.22 | 0.1991 | 0.3097 |
sports_science | 165 | 0.248485 | 24.85 | 0.1818 | 0.3212 |
traditional_chinese_medicine | 185 | 0.248649 | 24.86 | 0.1838 | 0.3189 |
virology | 169 | 0.248521 | 24.85 | 0.1834 | 0.3136 |
world_history | 161 | 0.242236 | 24.22 | 0.1801 | 0.3043 |
world_religions | 160 | 0.25625 | 25.62 | 0.1938 | 0.3187 |
agronomy | 169 | 0.242604 | 24.26 | 0.1775 | 0.3077 |
anatomy | 148 | 0.256757 | 25.68 | 0.1892 | 0.3378 |
ancient_chinese | 164 | 0.256098 | 25.61 | 0.1951 | 0.3232 |
arts | 160 | 0.25625 | 25.62 | 0.1938 | 0.3312 |
astronomy | 165 | 0.248485 | 24.85 | 0.1818 | 0.3152 |
business_ethics | 209 | 0.248804 | 24.88 | 0.1914 | 0.311 |
chinese_civil_service_exam | 160 | 0.25625 | 25.62 | 0.1938 | 0.3187 |
chinese_driving_rule | 131 | 0.251908 | 25.19 | 0.1756 | 0.3282 |
chinese_food_culture | 136 | 0.25 | 25 | 0.1838 | 0.3309 |
chinese_foreign_policy | 107 | 0.252336 | 25.23 | 0.1682 | 0.3364 |
chinese_history | 323 | 0.250774 | 25.08 | 0.2043 | 0.3003 |
chinese_literature | 204 | 0.254902 | 25.49 | 0.1961 | 0.3137 |
chinese_teacher_qualification | 179 | 0.251397 | 25.14 | 0.1899 | 0.3184 |
clinical_knowledge | 237 | 0.253165 | 25.32 | 0.1983 | 0.308 |
college_actuarial_science | 106 | 0.245283 | 24.53 | 0.1792 | 0.3302 |
college_education | 107 | 0.317757 | 31.78 | 0.2336 | 0.4019 |
college_engineering_hydrology | 106 | 0.301887 | 30.19 | 0.217 | 0.3868 |
college_law | 108 | 0.212963 | 21.3 | 0.1481 | 0.2963 |
college_mathematics | 105 | 0.219048 | 21.9 | 0.1429 | 0.2952 |
college_medical_statistics | 106 | 0.254717 | 25.47 | 0.1792 | 0.3396 |
college_medicine | 273 | 0.241758 | 24.18 | 0.1941 | 0.293 |
computer_science | 204 | 0.254902 | 25.49 | 0.1961 | 0.3186 |
computer_security | 171 | 0.251462 | 25.15 | 0.1871 | 0.3158 |
conceptual_physics | 147 | 0.251701 | 25.17 | 0.1837 | 0.3265 |
construction_project_management | 139 | 0.244604 | 24.46 | 0.1799 | 0.3165 |
economics | 159 | 0.245283 | 24.53 | 0.1761 | 0.3145 |
education | 163 | 0.251534 | 25.15 | 0.184 | 0.319 |
electrical_engineering | 172 | 0.25 | 25 | 0.186 | 0.314 |
elementary_chinese | 252 | 0.281746 | 28.17 | 0.2262 | 0.3413 |
elementary_commonsense | 198 | 0.242424 | 24.24 | 0.1869 | 0.303 |
elementary_information_and_technology | 238 | 0.273109 | 27.31 | 0.2143 | 0.3319 |
elementary_mathematics | 230 | 0.278261 | 27.83 | 0.2217 | 0.3348 |
ethnology | 135 | 0.251852 | 25.19 | 0.1778 | 0.3259 |
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