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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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