Dataset Viewer
Duplicate
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
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
python: string
total: int64
source: struct<git: struct<url: string, head: string, sha_verified_from: string, previous_substitute: string (... 2 chars omitted)
  child 0, git: struct<url: string, head: string, sha_verified_from: string, previous_substitute: string>
      child 0, url: string
      child 1, head: string
      child 2, sha_verified_from: string
      child 3, previous_substitute: string
passed: int64
all_passed: bool
sbom_written: bool
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
  child 0, aggregate_rule: string
  child 1, excluded: string
  child 2, reproduce: string
  child 3, thresholds_note: string
schema: string
failed_ids: list<item: null>
  child 0, item: null
failed: int64
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
  child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
      child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
          child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
              child 0, max_files: int64
              child 1, max_lines: int64
              child 2, min_lines: 
...
tion_environment_pinned: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
              child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                  child 0, detail: string
                  child 1, evidence_path: string
                  child 2, present: bool
          child 62, criteria_missing: list<item: null>
              child 0, item: null
          child 63, criteria_satisfied: int64
          child 64, criteria_total: int64
          child 65, framework_controls_verified: int64
          child 66, ladder: string
          child 67, observed_level: int64
          child 68, source: string
          child 69, trL9_claimed: bool
          child 70, trL9_reason: string
      child 1, id: string
      child 2, ok: bool
      child 3, status: string
      child 4, title: string
platform: string
root: string
version: string
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'url': Value('string'), 'head': Value('string'), 'sha_verified_from': Value('string'), 'previous_substitute': Value('string')}}}
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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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
              digest: string
              kind: string
              path: string
              payload_sha3: string
              prev: string
              seq: int64
              ts: timestamp[s]
              python: string
              total: int64
              source: struct<git: struct<url: string, head: string, sha_verified_from: string, previous_substitute: string (... 2 chars omitted)
                child 0, git: struct<url: string, head: string, sha_verified_from: string, previous_substitute: string>
                    child 0, url: string
                    child 1, head: string
                    child 2, sha_verified_from: string
                    child 3, previous_substitute: string
              passed: int64
              all_passed: bool
              sbom_written: bool
              method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
                child 0, aggregate_rule: string
                child 1, excluded: string
                child 2, reproduce: string
                child 3, thresholds_note: string
              schema: string
              failed_ids: list<item: null>
                child 0, item: null
              failed: int64
              benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
                child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
                    child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
                        child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
                            child 0, max_files: int64
                            child 1, max_lines: int64
                            child 2, min_lines: 
              ...
              tion_environment_pinned: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 5, reproducible_tests: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                            child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
                                child 0, detail: string
                                child 1, evidence_path: string
                                child 2, present: bool
                        child 62, criteria_missing: list<item: null>
                            child 0, item: null
                        child 63, criteria_satisfied: int64
                        child 64, criteria_total: int64
                        child 65, framework_controls_verified: int64
                        child 66, ladder: string
                        child 67, observed_level: int64
                        child 68, source: string
                        child 69, trL9_claimed: bool
                        child 70, trL9_reason: string
                    child 1, id: string
                    child 2, ok: bool
                    child 3, status: string
                    child 4, title: string
              platform: string
              root: string
              version: string
              to
              {'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
              ...
              '), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'url': Value('string'), 'head': Value('string'), 'sha_verified_from': Value('string'), 'previous_substitute': Value('string')}}}
              because column names don't match

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IEA

Using stable diffusion and segmentation anything models for image editing.

Category: SOLAR · Ecosystem: unknown · Vendored upstream: https://github.com/feizc/IEA · Pinned commit: bbce8fa8ad7b1bbd8735459495d4af4b7e914222 · Vendor: Anticloud FZ LLE

What This Project Is

Using stable diffusion and segmentation anything models for image editing.

This is the upstream project, vendored at the pinned commit above and packaged offline-first. The 12 improvements Anticloud adds live in anticloud/ and are described under Anticloud Overlay below. Every fact in this file traces to a file in this directory.

Installation

# See the upstream README (linked above) for build steps.

Usage

See the upstream README (linked above) for usage. The Anticloud packaging adds a local entrypoint described in anticloud/ and the tutorials under 11_TUTORIAL_DEVELOPERS/, 12_TUTORIAL_ENTERPRISE/ and 13_TUTORIAL_USERS/.

Anticloud Overlay

The 12 improvements live under anticloud/. Run the check suite from this directory:

python anticloud/bench/runner.py --cwd anticloud

Benchmark Results

Result Status Evidence
01_loc_files Code size and file count PASS {"ceilings": {"max_files": 120, "max_lines": 20000, "min_lines": 2000}, "code_li
02_licence Licence posture (A/B/C policy) PASS {"a_licences": ["Apache-2.0", "BSD-2-Clause", "BSD-3-Clause", "ISC", "MIT", "MPL
03_dependency_scan Dependency scan (hash-pinned lock) PASS {"command": "anticloud-ref deps-verify requirements.lock", "declared_in_pyprojec
04_sbom_cyclonedx SBOM (CycloneDX 1.5) PASS {"command": "python -c "import json;d=json.load(open('sbom.cdx.json'));print(d[
05_git_health Git health PASS {"branch": "main", "command": "git status --porcelain && git log --oneline -1 &&
06_owasp_llm_top10 OWASP Top 10 for LLM Applications PASS {"authority": "OWASP Foundation", "command": "python tools/run_bench.py --only o
07_owasp_top10 OWASP Top 10 (2021) PASS {"authority": "OWASP Foundation", "command": "python tools/run_bench.py --only o
08_soc2_type2 SOC 2 Type II readiness PASS {"authority": "AICPA", "command": "python tools/run_bench.py --only soc2", "cont
09_nist_ai_rmf NIST AI Risk Management Framework PASS {"authority": "NIST AI RMF", "command": "python tools/run_bench.py --only nist",
10_nist_sp_800_53 NIST SP 800-53 Rev. 5 PASS {"authority": "NIST", "command": "python tools/run_bench.py --only nist", "contr
11_nist_csf NIST Cybersecurity Framework 2.0 PASS {"authority": "NIST", "command": "python tools/run_bench.py --only nist", "contr
12_fedramp FedRAMP Rev. 5 PASS {"authority": "FedRAMP", "command": "python tools/run_bench.py --only fedramp",
13_pci_dss PCI DSS v4.0.1 PASS {"authority": "PCI SSC", "command": "python tools/run_bench.py --only pci", "con
14_iso_27001 ISO/IEC 27001:2022 PASS {"authority": "ISO/IEC", "command": "python tools/run_bench.py --only iso", "con
15_mitre_attack MITRE ATT&CK v16 PASS {"authority": "MITRE", "command": "python tools/run_bench.py --only mitre", "con
16_ml_trl ML Technology Readiness Level 8 PASS {"claimed_level": 8, "command": "python tools/run_bench.py --only 16 && cat docs

Suite: 16/16 passed · platform win32 · python 3.12.10 · method {'aggregate_rule': 'all_passed == AND over benchmarks[*].ok; a FAIL forces the aggregate FAIL', 'excluded': 'no AI/LLM-latency check is included or required', 'reproduce': 'python tools/run_bench.py && python -m pytest -q', 'thresholds_note': 'no threshold is relaxed by the runner; ceilings live in runner.py and are asserted independently in tests/test_project_metadata.py'}

Licence

The upstream licence is preserved in LICENSE and THIRD_PARTY_LICENSES/. The Anticloud overlay is licensed under Anticommons 0.1.0 (ANTICOMMONS_LICENSE.md).

Verification

SHA3-256 of BENCH.json:

8aedf422d3e0a90dd6526dbd444ab15672acb769d32e102e3d48560f2b4d4a3e

Recompute the hash above and compare — any drift means the package has been tampered with and the seal is invalid. This is a self-assessment; no external audit has been performed and a SOC 2 / NIST independent audit is committed post-funding.

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