Datasets:

---

Lukita

/

MPSAE-embeddings

Like 0

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
archive: string
backbone: string
batch_size: int64
dataset: struct<content_audited_splits: list<item: string>, exclude_self: bool, format: string, gallery_split (... 477 chars omitted)
  child 0, content_audited_splits: list<item: string>
      child 0, item: string
  child 1, exclude_self: bool
  child 2, format: string
  child 3, gallery_split: string
  child 4, label_vocabulary_sha256: string
  child 5, name: string
  child 6, protocol: string
  child 7, query_split: string
  child 8, relevance_key: string
  child 9, repository: string
  child 10, revision: string
  child 11, splits: struct<test: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>, tra (... 183 chars omitted)
      child 0, test: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
          child 0, ids_sha256: string
          child 1, labels_sha256: string
          child 2, rgb_sha256: string
          child 3, rows: int64
      child 1, train: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
          child 0, ids_sha256: string
          child 1, labels_sha256: string
          child 2, rgb_sha256: string
          child 3, rows: int64
      child 2, validation: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
          child 0, ids_sha256: string
          child 1, labels_sha256: string
          child 2, rgb_sha256: string
          child 3, rows: int64
  child 12, strict
...
ng
  child 1, labels: string
identity: string
ids_sha256: string
keys: list<item: string>
  child 0, item: string
labels_sha256: string
libraries: struct<PIL: string, ffcv: null, huggingface_hub: string, numpy: string, pyarrow: string, timm: strin (... 38 chars omitted)
  child 0, PIL: string
  child 1, ffcv: null
  child 2, huggingface_hub: string
  child 3, numpy: string
  child 4, pyarrow: string
  child 5, timm: string
  child 6, torch: string
  child 7, torchvision: string
precision: string
preprocessing: struct<augmentation: string, center_crop: int64, color: string, drop_last: bool, input_feature_l2_no (... 135 chars omitted)
  child 0, augmentation: string
  child 1, center_crop: int64
  child 2, color: string
  child 3, drop_last: bool
  child 4, input_feature_l2_normalization: bool
  child 5, interpolation: string
  child 6, mean: list<item: double>
      child 0, item: double
  child 7, resize_shorter_side: int64
  child 8, shuffle: bool
  child 9, std: list<item: double>
      child 0, item: double
recipe: string
rows: int64
sha256: string
source_weights_verified: bool
split: string
tf32: bool
training_on_target: bool
weights: struct<feature: string, file: string, img_size_override: null, repository: string, revision: string, (... 34 chars omitted)
  child 0, feature: string
  child 1, file: string
  child 2, img_size_override: null
  child 3, repository: string
  child 4, revision: string
  child 5, sha256: string
  child 6, timm_tag: string
written: timestamp[s]
to
{'text': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 810, in write_table
                  self.write_rows_on_file()  # in case there are buffered rows to write first
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 690, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._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 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              archive: string
              backbone: string
              batch_size: int64
              dataset: struct<content_audited_splits: list<item: string>, exclude_self: bool, format: string, gallery_split (... 477 chars omitted)
                child 0, content_audited_splits: list<item: string>
                    child 0, item: string
                child 1, exclude_self: bool
                child 2, format: string
                child 3, gallery_split: string
                child 4, label_vocabulary_sha256: string
                child 5, name: string
                child 6, protocol: string
                child 7, query_split: string
                child 8, relevance_key: string
                child 9, repository: string
                child 10, revision: string
                child 11, splits: struct<test: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>, tra (... 183 chars omitted)
                    child 0, test: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
                        child 0, ids_sha256: string
                        child 1, labels_sha256: string
                        child 2, rgb_sha256: string
                        child 3, rows: int64
                    child 1, train: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
                        child 0, ids_sha256: string
                        child 1, labels_sha256: string
                        child 2, rgb_sha256: string
                        child 3, rows: int64
                    child 2, validation: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
                        child 0, ids_sha256: string
                        child 1, labels_sha256: string
                        child 2, rgb_sha256: string
                        child 3, rows: int64
                child 12, strict
              ...
              ng
                child 1, labels: string
              identity: string
              ids_sha256: string
              keys: list<item: string>
                child 0, item: string
              labels_sha256: string
              libraries: struct<PIL: string, ffcv: null, huggingface_hub: string, numpy: string, pyarrow: string, timm: strin (... 38 chars omitted)
                child 0, PIL: string
                child 1, ffcv: null
                child 2, huggingface_hub: string
                child 3, numpy: string
                child 4, pyarrow: string
                child 5, timm: string
                child 6, torch: string
                child 7, torchvision: string
              precision: string
              preprocessing: struct<augmentation: string, center_crop: int64, color: string, drop_last: bool, input_feature_l2_no (... 135 chars omitted)
                child 0, augmentation: string
                child 1, center_crop: int64
                child 2, color: string
                child 3, drop_last: bool
                child 4, input_feature_l2_normalization: bool
                child 5, interpolation: string
                child 6, mean: list<item: double>
                    child 0, item: double
                child 7, resize_shorter_side: int64
                child 8, shuffle: bool
                child 9, std: list<item: double>
                    child 0, item: double
              recipe: string
              rows: int64
              sha256: string
              source_weights_verified: bool
              split: string
              tf32: bool
              training_on_target: bool
              weights: struct<feature: string, file: string, img_size_override: null, repository: string, revision: string, (... 34 chars omitted)
                child 0, feature: string
                child 1, file: string
                child 2, img_size_override: null
                child 3, repository: string
                child 4, revision: string
                child 5, sha256: string
                child 6, timm_tag: string
              written: timestamp[s]
              to
              {'text': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, 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 23 new columns ({'archive', 'libraries', 'tf32', 'ids_sha256', 'weights', 'batch_size', 'recipe', 'backbone', 'training_on_target', 'precision', 'split', 'sha256', 'keys', 'written', 'dim', 'dataset', 'preprocessing', 'dtypes', 'source_weights_verified', 'rows', 'labels_sha256', 'deviations', 'identity'}) and 1 missing columns ({'text'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/Lukita/MPSAE-embeddings/eurosat_dinov2_vits14_384/train.ids.json (at revision f67f3f36ea13e61bf70287a14a3f7df636131dda), ['hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_convnext_tiny_768/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_convnext_tiny_768/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_dinov2_vits14_384/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_dinov2_vits14_384/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_ff2048_2048/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_ff2048_2048/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_rn18_512/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_rn18_512/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_vit_b16_768/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_vit_b16_768/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_convnext_tiny_768/1K_train_convnext_tiny.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_convnext_tiny_768/1K_train_convnext_tiny.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_dinov2_vits14_384/1K_train_dinov2_vits14.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_dinov2_vits14_384/1K_train_dinov2_vits14.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_ff2048_2048/1K_train_ff2048.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_ff2048_2048/1K_train_ff2048.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_rn18_512/1K_train_rn18.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_rn18_512/1K_train_rn18.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_vit_b16_768/1K_train_vit_b16.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_vit_b16_768/1K_train_vit_b16.npz.json'], ['hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_convnext_tiny_768/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_convnext_tiny_768/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_dinov2_vits14_384/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_dinov2_vits14_384/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_ff2048_2048/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_ff2048_2048/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_rn18_512/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_rn18_512/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_vit_b16_768/train.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/eurosat_vit_b16_768/train.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_convnext_tiny_768/1K_train_convnext_tiny.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_convnext_tiny_768/1K_train_convnext_tiny.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_dinov2_vits14_384/1K_train_dinov2_vits14.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_dinov2_vits14_384/1K_train_dinov2_vits14.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_ff2048_2048/1K_train_ff2048.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_ff2048_2048/1K_train_ff2048.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_rn18_512/1K_train_rn18.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_rn18_512/1K_train_rn18.npz.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_vit_b16_768/1K_train_vit_b16.ids.json', 'hf://datasets/Lukita/MPSAE-embeddings@f67f3f36ea13e61bf70287a14a3f7df636131dda/imagenet_vit_b16_768/1K_train_vit_b16.npz.json']
              
              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)
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1876, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 830, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 690, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._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 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              archive: string
              backbone: string
              batch_size: int64
              dataset: struct<content_audited_splits: list<item: string>, exclude_self: bool, format: string, gallery_split (... 477 chars omitted)
                child 0, content_audited_splits: list<item: string>
                    child 0, item: string
                child 1, exclude_self: bool
                child 2, format: string
                child 3, gallery_split: string
                child 4, label_vocabulary_sha256: string
                child 5, name: string
                child 6, protocol: string
                child 7, query_split: string
                child 8, relevance_key: string
                child 9, repository: string
                child 10, revision: string
                child 11, splits: struct<test: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>, tra (... 183 chars omitted)
                    child 0, test: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
                        child 0, ids_sha256: string
                        child 1, labels_sha256: string
                        child 2, rgb_sha256: string
                        child 3, rows: int64
                    child 1, train: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
                        child 0, ids_sha256: string
                        child 1, labels_sha256: string
                        child 2, rgb_sha256: string
                        child 3, rows: int64
                    child 2, validation: struct<ids_sha256: string, labels_sha256: string, rgb_sha256: string, rows: int64>
                        child 0, ids_sha256: string
                        child 1, labels_sha256: string
                        child 2, rgb_sha256: string
                        child 3, rows: int64
                child 12, strict
              ...
              ng
                child 1, labels: string
              identity: string
              ids_sha256: string
              keys: list<item: string>
                child 0, item: string
              labels_sha256: string
              libraries: struct<PIL: string, ffcv: null, huggingface_hub: string, numpy: string, pyarrow: string, timm: strin (... 38 chars omitted)
                child 0, PIL: string
                child 1, ffcv: null
                child 2, huggingface_hub: string
                child 3, numpy: string
                child 4, pyarrow: string
                child 5, timm: string
                child 6, torch: string
                child 7, torchvision: string
              precision: string
              preprocessing: struct<augmentation: string, center_crop: int64, color: string, drop_last: bool, input_feature_l2_no (... 135 chars omitted)
                child 0, augmentation: string
                child 1, center_crop: int64
                child 2, color: string
                child 3, drop_last: bool
                child 4, input_feature_l2_normalization: bool
                child 5, interpolation: string
                child 6, mean: list<item: double>
                    child 0, item: double
                child 7, resize_shorter_side: int64
                child 8, shuffle: bool
                child 9, std: list<item: double>
                    child 0, item: double
              recipe: string
              rows: int64
              sha256: string
              source_weights_verified: bool
              split: string
              tf32: bool
              training_on_target: bool
              weights: struct<feature: string, file: string, img_size_override: null, repository: string, revision: string, (... 34 chars omitted)
                child 0, feature: string
                child 1, file: string
                child 2, img_size_override: null
                child 3, repository: string
                child 4, revision: string
                child 5, sha256: string
                child 6, timm_tag: string
              written: timestamp[s]
              to
              {'text': Value('string')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, 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 956, 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 1702, 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 1887, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset