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Datasets:
edithatogo
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nlp-policy-nz-cloud-ocr-pilots

Languages:
English
Tags:
new-zealand
ocr
benchmark
metadata-only
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
command: string
max_cost_usd: double
payload_policy: string
plan: struct<budget: struct<max_concurrency: int64, max_cost_usd: double, max_retries: int64>, collection_ (... 753 chars omitted)
  child 0, budget: struct<max_concurrency: int64, max_cost_usd: double, max_retries: int64>
      child 0, max_concurrency: int64
      child 1, max_cost_usd: double
      child 2, max_retries: int64
  child 1, collection_id: string
  child 2, ledger: list<item: struct<attempts: int64, content_address: string, error_code: null, item_id: string, outpu (... 49 chars omitted)
      child 0, item: struct<attempts: int64, content_address: string, error_code: null, item_id: string, output_digest: n (... 37 chars omitted)
          child 0, attempts: int64
          child 1, content_address: string
          child 2, error_code: null
          child 3, item_id: string
          child 4, output_digest: null
          child 5, shard_id: string
          child 6, state: string
  child 3, pipeline_version: string
  child 4, provenance: struct<orchestrator: string, payload_policy: string>
      child 0, orchestrator: string
      child 1, payload_policy: string
  child 5, quarantined_item_ids: list<item: null>
      child 0, item: null
  child 6, run_id: string
  child 7, schema_version: string
  child 8, work_manifest: struct<collection_id: string, items: list<item: struct<acquisition_mode: string, content_address: st (... 294 chars omitted)
      child 0, collection_id: string
      child 1, items: list<item: struct<acquisition_mode: string, content_address: string, content_allowed: bool, dataset_ (... 112 chars omitted)
          child 0, item: struct<acquisition_mode: string, content_address: string, content_allowed: bool, dataset_id: string, (... 100 chars omitted)
              child 0, acquisition_mode: string
              child 1, content_address: string
              child 2, content_allowed: bool
              child 3, dataset_id: string
              child 4, htid: string
              child 5, item_id: string
              child 6, publication_decision: string
              child 7, source_id: string
              child 8, source_url: string
      child 2, pipeline_version: string
      child 3, producer: string
      child 4, schema_version: string
      child 5, shards: list<item: struct<item_ids: list<item: string>, shard_id: string>>
          child 0, item: struct<item_ids: list<item: string>, shard_id: string>
              child 0, item_ids: list<item: string>
                  child 0, item: string
              child 1, shard_id: string
schema_version: string
shard_size: int64
status: string
volume_limit: int64
run_id: string
published_items: int64
collection_id: string
to
{'collection_id': Value('string'), 'command': Value('string'), 'max_cost_usd': Value('float64'), 'payload_policy': Value('string'), 'published_items': Value('int64'), 'run_id': Value('string'), 'schema_version': Value('string'), 'shard_size': Value('int64'), 'status': Value('string'), 'volume_limit': Value('int64')}
because column names don't match
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 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              command: string
              max_cost_usd: double
              payload_policy: string
              plan: struct<budget: struct<max_concurrency: int64, max_cost_usd: double, max_retries: int64>, collection_ (... 753 chars omitted)
                child 0, budget: struct<max_concurrency: int64, max_cost_usd: double, max_retries: int64>
                    child 0, max_concurrency: int64
                    child 1, max_cost_usd: double
                    child 2, max_retries: int64
                child 1, collection_id: string
                child 2, ledger: list<item: struct<attempts: int64, content_address: string, error_code: null, item_id: string, outpu (... 49 chars omitted)
                    child 0, item: struct<attempts: int64, content_address: string, error_code: null, item_id: string, output_digest: n (... 37 chars omitted)
                        child 0, attempts: int64
                        child 1, content_address: string
                        child 2, error_code: null
                        child 3, item_id: string
                        child 4, output_digest: null
                        child 5, shard_id: string
                        child 6, state: string
                child 3, pipeline_version: string
                child 4, provenance: struct<orchestrator: string, payload_policy: string>
                    child 0, orchestrator: string
                    child 1, payload_policy: string
                child 5, quarantined_item_ids: list<item: null>
                    child 0, item: null
                child 6, run_id: string
                child 7, schema_version: string
                child 8, work_manifest: struct<collection_id: string, items: list<item: struct<acquisition_mode: string, content_address: st (... 294 chars omitted)
                    child 0, collection_id: string
                    child 1, items: list<item: struct<acquisition_mode: string, content_address: string, content_allowed: bool, dataset_ (... 112 chars omitted)
                        child 0, item: struct<acquisition_mode: string, content_address: string, content_allowed: bool, dataset_id: string, (... 100 chars omitted)
                            child 0, acquisition_mode: string
                            child 1, content_address: string
                            child 2, content_allowed: bool
                            child 3, dataset_id: string
                            child 4, htid: string
                            child 5, item_id: string
                            child 6, publication_decision: string
                            child 7, source_id: string
                            child 8, source_url: string
                    child 2, pipeline_version: string
                    child 3, producer: string
                    child 4, schema_version: string
                    child 5, shards: list<item: struct<item_ids: list<item: string>, shard_id: string>>
                        child 0, item: struct<item_ids: list<item: string>, shard_id: string>
                            child 0, item_ids: list<item: string>
                                child 0, item: string
                            child 1, shard_id: string
              schema_version: string
              shard_size: int64
              status: string
              volume_limit: int64
              run_id: string
              published_items: int64
              collection_id: string
              to
              {'collection_id': Value('string'), 'command': Value('string'), 'max_cost_usd': Value('float64'), 'payload_policy': Value('string'), 'published_items': Value('int64'), 'run_id': Value('string'), 'schema_version': Value('string'), 'shard_size': Value('int64'), 'status': Value('string'), 'volume_limit': Value('int64')}
              because column names don't match

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NLP Policy NZ Cloud OCR Pilot Evidence

Registry status

Origin and provenance

  • Origin repository: https://github.com/edithatogo/nlp-policy-nz
  • Upstream source: Bounded metadata-only OCR pilot runs.
  • This card describes repository identity and relationships; it does not claim completeness beyond published manifests.

Rights boundary

Repository code, dataset compilation, and underlying source-record rights are separate. The registry currently records the rights state as metadata_only. Where rights vary by record or source, downstream users must inspect the published provenance and rights fields.

Existing dataset documentation

This dataset contains metadata-only run reports from a bounded OCR pilot. It is evidence for pipeline operations and benchmarking, not a full OCR corpus.

Scope

  • Run: track91-zero-cost-hf-20260715
  • Collection: cloud-ocr-baseline
  • Payload policy: metadata_only
  • Published items: 1
  • Full-text publication: not claimed
  • Maximum configured volume: 3 items

The reports include run identifiers, source references, content digests, publication decisions, and ledger counts. They do not contain a general corpus of source images or OCR text.

Provenance and rights

The pilot source is the pinned Tesseract documentation phototest example at https://raw.githubusercontent.com/tesseract-ocr/tessdoc/a8bf3c9241fe8241f7d13a796f9902c6f0a98087/examples/phototest.tif. Users must follow the source repository's applicable licence and attribution terms. Repository-owner rights approval is recorded in the companion registry evidence; this card does not assert provider acceptance or a DOI.

Intended use

Use these files to inspect bounded OCR orchestration, reconciliation, and provenance. They are not a validated production OCR benchmark or legal advice.

Reconciliation policy

This repository is retained only while its recorded role is distinct from the canonical dataset. Renames, deletion, visibility changes, and replacement of immutable DOI snapshots require explicit human review.
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