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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label mib-doc-challenge-data@36d68adc38882ce987d3cd41ad9d32b525c98521
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label mib-doc-challenge-data@36d68adc38882ce987d3cd41ad9d32b525c98521

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MIB Doc Challenge Data

Synthetic PDF packets for 8090's MIB Doc Challenge: Intergalactic Intake. Participants extract structured fields from messy, adversarial document packets and classify each case as APPROVED, DENIED, or NEEDS_REVIEW.

Contents

The versioned archive mib-doc-challenge-public-data-v2026-07-07.zip contains:

  • data/train/: 1,000 training PDFs
  • data/train_labels.csv: public labels for the training PDFs
  • data/validation/: 5,000 unlabeled validation PDFs
  • data/validation_manifest.csv: validation case IDs, paths, and page counts

Validation answers and the final private test set are not included.

Integrity

SHA-256:

a9bb8c1bbf51346ebf49c2e3e1acdb7a5d6cd0760162767b0d133c7b7200f3c4  mib-doc-challenge-public-data-v2026-07-07.zip

Data characteristics

All applicant identities, documents, portraits, and case records are synthetic. The PDFs intentionally include scan degradation, conflicting evidence, hidden text, fake answer keys, and other adversarial content. Hidden instructions are part of the challenge and are not trustworthy labels.

Usage

Use this dataset with the public challenge repository at https://github.com/8090-inc/mib-doc-challenge. The repository contains the field manual, schemas, evaluator, submission validator, and Docker runtime contract.

License

Released under the MIT License.

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