Datasets:

---

yasharde

/

Consumer-Fairness-in-Embedding-RAG-Data

like 0

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 771 new columns ({'dim_37', 'dim_760', 'dim_58', 'dim_387', 'dim_569', 'dim_549', 'dim_149', 'dim_284', 'dim_148', 'dim_327', 'dim_396', 'dim_533', 'dim_622', 'dim_436', 'dim_489', 'dim_324', 'dim_249', 'dim_206', 'dim_157', 'dim_439', 'dim_440', 'dim_24', 'dim_531', 'dim_368', 'dim_669', 'dim_744', 'dim_460', 'dim_702', 'dim_415', 'dim_20', 'dim_271', 'dim_304', 'dim_274', 'dim_660', 'dim_26', 'dim_278', 'dim_277', 'dim_298', 'dim_337', 'dim_253', 'dim_186', 'dim_137', 'dim_239', 'dim_490', 'dim_196', 'dim_417', 'dim_93', 'dim_190', 'dim_236', 'title', 'dim_348', 'dim_552', 'dim_637', 'dim_504', 'dim_215', 'dim_125', 'dim_25', 'dim_566', 'dim_96', 'dim_634', 'dim_134', 'dim_151', 'dim_537', 'dim_732', 'dim_303', 'dim_709', 'dim_576', 'dim_131', 'dim_259', 'dim_635', 'dim_23', 'dim_671', 'dim_741', 'dim_57', 'dim_496', 'dim_312', 'dim_350', 'dim_446', 'dim_83', 'dim_749', 'dim_449', 'dim_534', 'dim_42', 'dim_575', 'dim_640', 'dim_344', 'dim_753', 'dim_321', 'dim_129', 'dim_225', 'dim_711', 'dim_84', 'dim_329', 'dim_458', 'dim_260', 'dim_70', 'dim_561', 'dim_313', 'dim_528', 'dim_378', 'dim_103', 'dim_185', 'dim_55', 'dim_582', 'dim_652', 'dim_145', 'dim_712', 'dim_495', 'dim_150', 'dim_645', 'dim_518', 'dim_692', 'dim_465', 'dim_551', 'dim_250', 'dim_466', 'dim_15', 'dim_587', 'dim_628', 'dim_516', 'dim_522', 'dim_282', 'dim_626', 'dim_488', 'dim_403', 'dim_12', 'dim_124', 'dim_426', 'dim_553', 'dim_650', 'dim_585', 'dim_41', 'dim_410', 'dim_737', 'dim_392', 'dim_456', 'dim_618', 'dim_556', '
...
_289', 'dim_592', 'dim_649', 'dim_133', 'dim_521', 'dim_565', 'dim_505', 'dim_212', 'dim_290', 'dim_471', 'dim_680', 'dim_700', 'dim_693', 'dim_541', 'dim_69', 'dim_445', 'dim_686', 'dim_347', 'dim_759', 'dim_429', 'dim_210', 'dim_229', 'dim_510', 'dim_615', 'dim_291', 'dim_162', 'dim_430', 'dim_713', 'dim_655', 'dim_213', 'dim_314', 'dim_400', 'dim_320', 'dim_710', 'dim_345', 'dim_742', 'dim_366', 'dim_493', 'dim_707', 'dim_374', 'dim_670', 'dim_175', 'dim_573', 'dim_590', 'dim_178', 'dim_40', 'dim_407', 'dim_497', 'dim_509', 'dim_85', 'dim_263', 'dim_470', 'dim_156', 'dim_194', 'dim_240', 'dim_571', 'dim_677', 'dim_104', 'dim_199', 'dim_316', 'dim_311', 'dim_545', 'dim_601', 'dim_621', 'dim_699', 'dim_740', 'dim_86', 'dim_544', 'dim_389', 'dim_295', 'dim_492', 'dim_527', 'dim_681', 'movieId', 'dim_193', 'dim_611', 'dim_307', 'dim_78', 'dim_123', 'dim_412', 'dim_254', 'dim_424', 'dim_464', 'dim_619', 'dim_362', 'dim_5', 'dim_283', 'dim_121', 'dim_138', 'dim_128', 'dim_299', 'dim_636', 'dim_437', 'dim_384', 'dim_252', 'dim_694', 'dim_735', 'dim_118', 'dim_722', 'dim_82', 'dim_570', 'dim_372', 'dim_477', 'dim_443', 'dim_461', 'dim_375', 'dim_68', 'dim_491', 'dim_568', 'dim_406', 'dim_200', 'dim_662', 'dim_341', 'dim_638', 'dim_335', 'dim_336', 'dim_597', 'dim_77', 'dim_447', 'dim_67', 'dim_51', 'dim_169', 'dim_581', 'dim_674', 'dim_29', 'dim_220', 'dim_152', 'dim_207', 'dim_675', 'dim_192', 'dim_140', 'dim_292', 'dim_288', 'dim_623', 'dim_717', 'dim_459', 'dim_514', 'dim_305'}) and 32 missing columns ({'longitude', 'python_version', 'timestamp', 'run_id', 'ram_total_size', 'gpu_count', 'emissions', 'gpu_energy', 'cloud_provider', 'codecarbon_version', 'on_cloud', 'region', 'project_name', 'gpu_model', 'ram_energy', 'os', 'energy_consumed', 'experiment_id', 'cpu_energy', 'country_name', 'cpu_power', 'emissions_rate', 'cloud_region', 'gpu_power', 'cpu_model', 'duration', 'ram_power', 'cpu_count', 'country_iso_code', 'latitude', 'tracking_mode', 'pue'}).

This happened while the csv dataset builder was generating data using

hf://datasets/yasharde/Consumer-Fairness-in-Embedding-RAG-Data/ml_1m_8core_gemini_sample2784.csv (at revision 06d488f5f73f6c85cbd39f8a55b167d577e0959e)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              movieId: int64
              title: string
              genres: string
              dim_0: double
              dim_1: double
              dim_2: double
              dim_3: double
              dim_4: double
              dim_5: double
              dim_6: double
              dim_7: double
              dim_8: double
              dim_9: double
              dim_10: double
              dim_11: double
              dim_12: double
              dim_13: double
              dim_14: double
              dim_15: double
              dim_16: double
              dim_17: double
              dim_18: double
              dim_19: double
              dim_20: double
              dim_21: double
              dim_22: double
              dim_23: double
              dim_24: double
              dim_25: double
              dim_26: double
              dim_27: double
              dim_28: double
              dim_29: double
              dim_30: double
              dim_31: double
              dim_32: double
              dim_33: double
              dim_34: double
              dim_35: double
              dim_36: double
              dim_37: double
              dim_38: double
              dim_39: double
              dim_40: double
              dim_41: double
              dim_42: double
              dim_43: double
              dim_44: double
              dim_45: double
              dim_46: double
              dim_47: double
              dim_48: double
              dim_49: double
              dim_50: double
              dim_51: double
              dim_52: double
              dim_53: double
              dim_54: double
              dim_55: double
              dim_56: double
              dim_57: double
              dim_58: double
              dim_59: double
              dim_60: double
              dim_61: double
              dim_62: double
              dim_63: double
              dim_64: double
              dim_65: double
              dim_66: double
              dim_67: double
              dim_68: double
              dim_69: double
              dim_70: double
              dim_71: double
              dim_72: double
              dim_73: double
              dim_74: double
              dim_75: double
              dim_76: double
              dim_77: double
              dim_78: double
              dim_79: double
              dim_80: double
              dim_81: double
              dim_82: double
              dim_83: double
              dim_84: double
              dim_85: double
              dim_86: double
              dim_87: double
              dim_88: double
              dim_89: double
              dim_90: double
              dim_91: double
              dim_92: double
              dim_93: double
              dim_94: double
              dim_95: double
              dim_96: double
              dim_97: dou
              ...
              ble
              dim_681: double
              dim_682: double
              dim_683: double
              dim_684: double
              dim_685: double
              dim_686: double
              dim_687: double
              dim_688: double
              dim_689: double
              dim_690: double
              dim_691: double
              dim_692: double
              dim_693: double
              dim_694: double
              dim_695: double
              dim_696: double
              dim_697: double
              dim_698: double
              dim_699: double
              dim_700: double
              dim_701: double
              dim_702: double
              dim_703: double
              dim_704: double
              dim_705: double
              dim_706: double
              dim_707: double
              dim_708: double
              dim_709: double
              dim_710: double
              dim_711: double
              dim_712: double
              dim_713: double
              dim_714: double
              dim_715: double
              dim_716: double
              dim_717: double
              dim_718: double
              dim_719: double
              dim_720: double
              dim_721: double
              dim_722: double
              dim_723: double
              dim_724: double
              dim_725: double
              dim_726: double
              dim_727: double
              dim_728: double
              dim_729: double
              dim_730: double
              dim_731: double
              dim_732: double
              dim_733: double
              dim_734: double
              dim_735: double
              dim_736: double
              dim_737: double
              dim_738: double
              dim_739: double
              dim_740: double
              dim_741: double
              dim_742: double
              dim_743: double
              dim_744: double
              dim_745: double
              dim_746: double
              dim_747: double
              dim_748: double
              dim_749: double
              dim_750: double
              dim_751: double
              dim_752: double
              dim_753: double
              dim_754: double
              dim_755: double
              dim_756: double
              dim_757: double
              dim_758: double
              dim_759: double
              dim_760: double
              dim_761: double
              dim_762: double
              dim_763: double
              dim_764: double
              dim_765: double
              dim_766: double
              dim_767: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 88695
              to
              {'timestamp': Value(dtype='string', id=None), 'project_name': Value(dtype='string', id=None), 'run_id': Value(dtype='string', id=None), 'experiment_id': Value(dtype='string', id=None), 'duration': Value(dtype='float64', id=None), 'emissions': Value(dtype='float64', id=None), 'emissions_rate': Value(dtype='float64', id=None), 'cpu_power': Value(dtype='float64', id=None), 'gpu_power': Value(dtype='float64', id=None), 'ram_power': Value(dtype='float64', id=None), 'cpu_energy': Value(dtype='float64', id=None), 'gpu_energy': Value(dtype='float64', id=None), 'ram_energy': Value(dtype='float64', id=None), 'energy_consumed': Value(dtype='float64', id=None), 'country_name': Value(dtype='string', id=None), 'country_iso_code': Value(dtype='string', id=None), 'region': Value(dtype='float64', id=None), 'cloud_provider': Value(dtype='float64', id=None), 'cloud_region': Value(dtype='float64', id=None), 'os': Value(dtype='string', id=None), 'python_version': Value(dtype='string', id=None), 'codecarbon_version': Value(dtype='string', id=None), 'cpu_count': Value(dtype='int64', id=None), 'cpu_model': Value(dtype='string', id=None), 'gpu_count': Value(dtype='int64', id=None), 'gpu_model': Value(dtype='string', id=None), 'longitude': Value(dtype='float64', id=None), 'latitude': Value(dtype='float64', id=None), 'ram_total_size': Value(dtype='float64', id=None), 'tracking_mode': Value(dtype='string', id=None), 'on_cloud': Value(dtype='string', id=None), 'pue': Value(dtype='float64', id=None)}
              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 1438, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 771 new columns ({'dim_37', 'dim_760', 'dim_58', 'dim_387', 'dim_569', 'dim_549', 'dim_149', 'dim_284', 'dim_148', 'dim_327', 'dim_396', 'dim_533', 'dim_622', 'dim_436', 'dim_489', 'dim_324', 'dim_249', 'dim_206', 'dim_157', 'dim_439', 'dim_440', 'dim_24', 'dim_531', 'dim_368', 'dim_669', 'dim_744', 'dim_460', 'dim_702', 'dim_415', 'dim_20', 'dim_271', 'dim_304', 'dim_274', 'dim_660', 'dim_26', 'dim_278', 'dim_277', 'dim_298', 'dim_337', 'dim_253', 'dim_186', 'dim_137', 'dim_239', 'dim_490', 'dim_196', 'dim_417', 'dim_93', 'dim_190', 'dim_236', 'title', 'dim_348', 'dim_552', 'dim_637', 'dim_504', 'dim_215', 'dim_125', 'dim_25', 'dim_566', 'dim_96', 'dim_634', 'dim_134', 'dim_151', 'dim_537', 'dim_732', 'dim_303', 'dim_709', 'dim_576', 'dim_131', 'dim_259', 'dim_635', 'dim_23', 'dim_671', 'dim_741', 'dim_57', 'dim_496', 'dim_312', 'dim_350', 'dim_446', 'dim_83', 'dim_749', 'dim_449', 'dim_534', 'dim_42', 'dim_575', 'dim_640', 'dim_344', 'dim_753', 'dim_321', 'dim_129', 'dim_225', 'dim_711', 'dim_84', 'dim_329', 'dim_458', 'dim_260', 'dim_70', 'dim_561', 'dim_313', 'dim_528', 'dim_378', 'dim_103', 'dim_185', 'dim_55', 'dim_582', 'dim_652', 'dim_145', 'dim_712', 'dim_495', 'dim_150', 'dim_645', 'dim_518', 'dim_692', 'dim_465', 'dim_551', 'dim_250', 'dim_466', 'dim_15', 'dim_587', 'dim_628', 'dim_516', 'dim_522', 'dim_282', 'dim_626', 'dim_488', 'dim_403', 'dim_12', 'dim_124', 'dim_426', 'dim_553', 'dim_650', 'dim_585', 'dim_41', 'dim_410', 'dim_737', 'dim_392', 'dim_456', 'dim_618', 'dim_556', '
              ...
              _289', 'dim_592', 'dim_649', 'dim_133', 'dim_521', 'dim_565', 'dim_505', 'dim_212', 'dim_290', 'dim_471', 'dim_680', 'dim_700', 'dim_693', 'dim_541', 'dim_69', 'dim_445', 'dim_686', 'dim_347', 'dim_759', 'dim_429', 'dim_210', 'dim_229', 'dim_510', 'dim_615', 'dim_291', 'dim_162', 'dim_430', 'dim_713', 'dim_655', 'dim_213', 'dim_314', 'dim_400', 'dim_320', 'dim_710', 'dim_345', 'dim_742', 'dim_366', 'dim_493', 'dim_707', 'dim_374', 'dim_670', 'dim_175', 'dim_573', 'dim_590', 'dim_178', 'dim_40', 'dim_407', 'dim_497', 'dim_509', 'dim_85', 'dim_263', 'dim_470', 'dim_156', 'dim_194', 'dim_240', 'dim_571', 'dim_677', 'dim_104', 'dim_199', 'dim_316', 'dim_311', 'dim_545', 'dim_601', 'dim_621', 'dim_699', 'dim_740', 'dim_86', 'dim_544', 'dim_389', 'dim_295', 'dim_492', 'dim_527', 'dim_681', 'movieId', 'dim_193', 'dim_611', 'dim_307', 'dim_78', 'dim_123', 'dim_412', 'dim_254', 'dim_424', 'dim_464', 'dim_619', 'dim_362', 'dim_5', 'dim_283', 'dim_121', 'dim_138', 'dim_128', 'dim_299', 'dim_636', 'dim_437', 'dim_384', 'dim_252', 'dim_694', 'dim_735', 'dim_118', 'dim_722', 'dim_82', 'dim_570', 'dim_372', 'dim_477', 'dim_443', 'dim_461', 'dim_375', 'dim_68', 'dim_491', 'dim_568', 'dim_406', 'dim_200', 'dim_662', 'dim_341', 'dim_638', 'dim_335', 'dim_336', 'dim_597', 'dim_77', 'dim_447', 'dim_67', 'dim_51', 'dim_169', 'dim_581', 'dim_674', 'dim_29', 'dim_220', 'dim_152', 'dim_207', 'dim_675', 'dim_192', 'dim_140', 'dim_292', 'dim_288', 'dim_623', 'dim_717', 'dim_459', 'dim_514', 'dim_305'}) and 32 missing columns ({'longitude', 'python_version', 'timestamp', 'run_id', 'ram_total_size', 'gpu_count', 'emissions', 'gpu_energy', 'cloud_provider', 'codecarbon_version', 'on_cloud', 'region', 'project_name', 'gpu_model', 'ram_energy', 'os', 'energy_consumed', 'experiment_id', 'cpu_energy', 'country_name', 'cpu_power', 'emissions_rate', 'cloud_region', 'gpu_power', 'cpu_model', 'duration', 'ram_power', 'cpu_count', 'country_iso_code', 'latitude', 'tracking_mode', 'pue'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/yasharde/Consumer-Fairness-in-Embedding-RAG-Data/ml_1m_8core_gemini_sample2784.csv (at revision 06d488f5f73f6c85cbd39f8a55b167d577e0959e)
              
              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)