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huggingface_terminal_github_fetch_3554-model-registry-56834546

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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
intake: list<item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security (... 49 chars omitted)
  child 0, item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security_review: st (... 37 chars omitted)
      child 0, model_id: string
      child 1, model_name: string
      child 2, domain: string
      child 3, risk_level: string
      child 4, security_review: string
      child 5, version: string
      child 6, owner: string
registry: string
models: list<item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security (... 94 chars omitted)
  child 0, item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security_review: st (... 82 chars omitted)
      child 0, model_id: string
      child 1, model_name: string
      child 2, domain: string
      child 3, risk_level: string
      child 4, security_review: string
      child 5, release_status: string
      child 6, version: string
      child 7, owner: string
      child 8, traffic_pct: double
to
{'registry': Value('string'), 'models': List({'model_id': Value('string'), 'model_name': Value('string'), 'domain': Value('string'), 'risk_level': Value('string'), 'security_review': Value('string'), 'release_status': Value('string'), 'version': Value('string'), 'owner': Value('string'), 'traffic_pct': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              intake: list<item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security (... 49 chars omitted)
                child 0, item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security_review: st (... 37 chars omitted)
                    child 0, model_id: string
                    child 1, model_name: string
                    child 2, domain: string
                    child 3, risk_level: string
                    child 4, security_review: string
                    child 5, version: string
                    child 6, owner: string
              registry: string
              models: list<item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security (... 94 chars omitted)
                child 0, item: struct<model_id: string, model_name: string, domain: string, risk_level: string, security_review: st (... 82 chars omitted)
                    child 0, model_id: string
                    child 1, model_name: string
                    child 2, domain: string
                    child 3, risk_level: string
                    child 4, security_review: string
                    child 5, release_status: string
                    child 6, version: string
                    child 7, owner: string
                    child 8, traffic_pct: double
              to
              {'registry': Value('string'), 'models': List({'model_id': Value('string'), 'model_name': Value('string'), 'domain': Value('string'), 'risk_level': Value('string'), 'security_review': Value('string'), 'release_status': Value('string'), 'version': Value('string'), 'owner': Value('string'), 'traffic_pct': Value('float64')})}
              because column names don't match

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Acme Systems - ML Model Release Registry

Owner: MLOps Governance Team Registry kind: dataset (Hugging Face Hub) Audit cadence: monthly release-gate audit

Purpose

This dataset is the single source of truth for the production ML model catalog of Acme Systems. Every release cycle the governance analyst:

  1. Reads the active catalog from registry/models.json.
  2. Reads candidate models submitted this cycle from intake/pending_models.json.
  3. Applies the release policy below.
  4. Updates the catalog, archives retired models and commits an audit report.

Folder layout

path purpose
registry/models.json active model catalog (authoritative)
intake/pending_models.json candidate models for the current cycle (read-only input)
archive/deprecated_models.json retired models archive
audit/release_audit_YYYY-MM-DD.json per-cycle compliance report

Release policy (decision table)

For every model the effective release_status must follow:

security_review risk_level required release_status
failed / flagged any blocked
pending any review
passed low / medium staging or production
passed high / critical review (plus release_approval: "pending")

Reconciliation: any active catalog entry whose release_status violates the decision table must be corrected to the required release_status value. Never modify security_review or risk_level.

Intake derivation: when a candidate from intake/pending_models.json is added to the active catalog, derive its release_status as follows:

  • security_review: failed or flagged -> blocked
  • security_review: pending -> review
  • security_review: passed and risk_level: low/medium -> staging
  • security_review: passed and risk_level: high/critical -> review plus release_approval: "pending"

Archival: release_status = deprecated entries must be moved from the active catalog into archive/deprecated_models.json (adding archived_at with the audit date) and removed from registry/models.json.

Note

Field traffic_pct is informational only and does not participate in the gate.
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