Datasets:

---

Haoxuan1111

/

Datacards-annotate

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 3 new columns ({'generation_timestamp', 'generation_status', 'generated_data_card'}) and 1 missing columns ({'arxivId'}).

This happened while the json dataset builder was generating data using

hf://datasets/Haoxuan1111/Datacards-annotate/sampled_gemini_dataset_cards_part1.json (at revision b785fa8ccc536fe2aff72f95d2c4ba1126010ec8)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              datasetId: string
              generated_data_card: struct<bias_and_fairness: struct<bias_mitigation: struct<confidence: string, content: string>, demographic_representation: struct<confidence: string, content: string>, fairness_considerations: struct<confidence: string, content: string>, geographic_coverage: struct<confidence: string, content: string>, known_biases: struct<confidence: string, content: string>, temporal_coverage: struct<confidence: string, content: string>>, content_analysis: struct<content_moderation: struct<confidence: string, content: string>, content_types: struct<confidence: string, content: string>, cultural_sensitivity: struct<confidence: string, content: string>, harmful_content: struct<confidence: string, content: string>, misinformation_risks: struct<confidence: string, content: string>, toxicity_analysis: struct<confidence: string, content: string>>, data_collection: struct<collection_process: struct<confidence: string, content: string>, collection_timeframe: struct<confidence: string, content: string>, consent_process: struct<confidence: string, content: string>, data_sources: struct<confidence: string, content: string>, data_validation: struct<confidence: string, content: string>, ethical_review: struct<confidence: string, content: string>>, data_processing: struct<cleaning_procedures: struct<confidence: string, content: string>, deduplication: struct<confidence: string, content: string>, filtering_criteria: struct<confidence: string, content: string>, labeli
              ...
              udonymization: struct<confidence: string, content: string>, data_security: struct<confidence: string, content: string>, personally_identifiable_info: struct<confidence: string, content: string>, privacy_protection_measures: struct<confidence: string, content: string>, retention_deletion: struct<confidence: string, content: string>, sensitive_information: struct<confidence: string, content: string>>
                    child 0, anonymization_pseudonymization: struct<confidence: string, content: string>
                        child 0, confidence: string
                        child 1, content: string
                    child 1, data_security: struct<confidence: string, content: string>
                        child 0, confidence: string
                        child 1, content: string
                    child 2, personally_identifiable_info: struct<confidence: string, content: string>
                        child 0, confidence: string
                        child 1, content: string
                    child 3, privacy_protection_measures: struct<confidence: string, content: string>
                        child 0, confidence: string
                        child 1, content: string
                    child 4, retention_deletion: struct<confidence: string, content: string>
                        child 0, confidence: string
                        child 1, content: string
                    child 5, sensitive_information: struct<confidence: string, content: string>
                        child 0, confidence: string
                        child 1, content: string
              generation_status: string
              generation_timestamp: string
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 631
              to
              {'datasetId': Value('string'), 'arxivId': Value('string')}
              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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1833, 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 3 new columns ({'generation_timestamp', 'generation_status', 'generated_data_card'}) and 1 missing columns ({'arxivId'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/Haoxuan1111/Datacards-annotate/sampled_gemini_dataset_cards_part1.json (at revision b785fa8ccc536fe2aff72f95d2c4ba1126010ec8)
              
              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)