Dataset Viewer
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
$schema: string
$id: string
title: string
type: string
additionalProperties: bool
required: list<item: string>
child 0, item: string
properties: struct<record_id: struct<type: string, minLength: int64>, schema_version: struct<const: string>, cap (... 2214 chars omitted)
child 0, record_id: struct<type: string, minLength: int64>
child 0, type: string
child 1, minLength: int64
child 1, schema_version: struct<const: string>
child 0, const: string
child 2, capture_timestamp_utc: struct<type: string, format: string>
child 0, type: string
child 1, format: string
child 3, coordinate: struct<type: string, additionalProperties: bool, required: list<item: string>, properties: struct<la (... 225 chars omitted)
child 0, type: string
child 1, additionalProperties: bool
child 2, required: list<item: string>
child 0, item: string
child 3, properties: struct<latitude: struct<type: list<item: string>, minimum: int64, maximum: int64>, longitude: struct (... 133 chars omitted)
child 0, latitude: struct<type: list<item: string>, minimum: int64, maximum: int64>
child 0, type: list<item: string>
child 0, item: string
child 1, minimum: int64
child 2, maximum: int64
child 1, longitude: struct<type: list<item: string>, minimum: int64, maximum: int64>
child 0, type: list<item: string>
child 0, item: string
...
, theta_required_deg: double
child 3, theta_fail_deg: int64
child 4, angle_margin_deg: double
coordinate: struct<latitude: double, longitude: double, precision_degrees: double, privacy: string>
child 0, latitude: double
child 1, longitude: double
child 2, precision_degrees: double
child 3, privacy: string
protocol: struct<version: string, evidence_strategy: string, lift_profile_documented: string>
child 0, version: string
child 1, evidence_strategy: string
child 2, lift_profile_documented: string
weather: struct<ambient_temp_f: int64, source: string, observed_at: timestamp[s]>
child 0, ambient_temp_f: int64
child 1, source: string
child 2, observed_at: timestamp[s]
record_id: string
labels: struct<label_source: string, review_status: string, human_verified: bool, result: string, failure_cu (... 162 chars omitted)
child 0, label_source: string
child 1, review_status: string
child 2, human_verified: bool
child 3, result: string
child 4, failure_cue: string
child 5, geometry_result: string
child 6, fastener_access_achieved: string
child 7, collateral_damage_observed: string
child 8, released_lays_flat: string
child 9, resealed_after_test: string
llm_grounding: struct<scope: string, may_summarize: list<item: string>, must_not_infer: list<item: string>>
child 0, scope: string
child 1, may_summarize: list<item: string>
child 0, item: string
child 2, must_not_infer: list<item: string>
child 0, item: string
synthetic: bool
to
{'record_id': Value('string'), 'schema_version': Value('string'), 'capture_timestamp_utc': Value('timestamp[s]'), 'coordinate': {'latitude': Value('float64'), 'longitude': Value('float64'), 'precision_degrees': Value('float64'), 'privacy': Value('string')}, 'weather': {'ambient_temp_f': Value('int64'), 'source': Value('string'), 'observed_at': Value('timestamp[s]')}, 'protocol': {'version': Value('string'), 'evidence_strategy': Value('string'), 'lift_profile_documented': Value('string')}, 'measurements': {'nail_depth_in': Value('int64'), 'clearance_required_in': Value('int64'), 'theta_required_deg': Value('float64'), 'theta_fail_deg': Value('int64'), 'angle_margin_deg': Value('float64')}, 'labels': {'label_source': Value('string'), 'review_status': Value('string'), 'human_verified': Value('bool'), 'result': Value('string'), 'failure_cue': Value('string'), 'geometry_result': Value('string'), 'fastener_access_achieved': Value('string'), 'collateral_damage_observed': Value('string'), 'released_lays_flat': Value('string'), 'resealed_after_test': Value('string')}, 'evidence': {'count': Value('int64'), 'sha256_hashes': List(Value('null'))}, 'llm_grounding': {'scope': Value('string'), 'may_summarize': List(Value('string')), 'must_not_infer': List(Value('string'))}, 'synthetic': Value('bool')}
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
$schema: string
$id: string
title: string
type: string
additionalProperties: bool
required: list<item: string>
child 0, item: string
properties: struct<record_id: struct<type: string, minLength: int64>, schema_version: struct<const: string>, cap (... 2214 chars omitted)
child 0, record_id: struct<type: string, minLength: int64>
child 0, type: string
child 1, minLength: int64
child 1, schema_version: struct<const: string>
child 0, const: string
child 2, capture_timestamp_utc: struct<type: string, format: string>
child 0, type: string
child 1, format: string
child 3, coordinate: struct<type: string, additionalProperties: bool, required: list<item: string>, properties: struct<la (... 225 chars omitted)
child 0, type: string
child 1, additionalProperties: bool
child 2, required: list<item: string>
child 0, item: string
child 3, properties: struct<latitude: struct<type: list<item: string>, minimum: int64, maximum: int64>, longitude: struct (... 133 chars omitted)
child 0, latitude: struct<type: list<item: string>, minimum: int64, maximum: int64>
child 0, type: list<item: string>
child 0, item: string
child 1, minimum: int64
child 2, maximum: int64
child 1, longitude: struct<type: list<item: string>, minimum: int64, maximum: int64>
child 0, type: list<item: string>
child 0, item: string
...
, theta_required_deg: double
child 3, theta_fail_deg: int64
child 4, angle_margin_deg: double
coordinate: struct<latitude: double, longitude: double, precision_degrees: double, privacy: string>
child 0, latitude: double
child 1, longitude: double
child 2, precision_degrees: double
child 3, privacy: string
protocol: struct<version: string, evidence_strategy: string, lift_profile_documented: string>
child 0, version: string
child 1, evidence_strategy: string
child 2, lift_profile_documented: string
weather: struct<ambient_temp_f: int64, source: string, observed_at: timestamp[s]>
child 0, ambient_temp_f: int64
child 1, source: string
child 2, observed_at: timestamp[s]
record_id: string
labels: struct<label_source: string, review_status: string, human_verified: bool, result: string, failure_cu (... 162 chars omitted)
child 0, label_source: string
child 1, review_status: string
child 2, human_verified: bool
child 3, result: string
child 4, failure_cue: string
child 5, geometry_result: string
child 6, fastener_access_achieved: string
child 7, collateral_damage_observed: string
child 8, released_lays_flat: string
child 9, resealed_after_test: string
llm_grounding: struct<scope: string, may_summarize: list<item: string>, must_not_infer: list<item: string>>
child 0, scope: string
child 1, may_summarize: list<item: string>
child 0, item: string
child 2, must_not_infer: list<item: string>
child 0, item: string
synthetic: bool
to
{'record_id': Value('string'), 'schema_version': Value('string'), 'capture_timestamp_utc': Value('timestamp[s]'), 'coordinate': {'latitude': Value('float64'), 'longitude': Value('float64'), 'precision_degrees': Value('float64'), 'privacy': Value('string')}, 'weather': {'ambient_temp_f': Value('int64'), 'source': Value('string'), 'observed_at': Value('timestamp[s]')}, 'protocol': {'version': Value('string'), 'evidence_strategy': Value('string'), 'lift_profile_documented': Value('string')}, 'measurements': {'nail_depth_in': Value('int64'), 'clearance_required_in': Value('int64'), 'theta_required_deg': Value('float64'), 'theta_fail_deg': Value('int64'), 'angle_margin_deg': Value('float64')}, 'labels': {'label_source': Value('string'), 'review_status': Value('string'), 'human_verified': Value('bool'), 'result': Value('string'), 'failure_cue': Value('string'), 'geometry_result': Value('string'), 'fastener_access_achieved': Value('string'), 'collateral_damage_observed': Value('string'), 'released_lays_flat': Value('string'), 'resealed_after_test': Value('string')}, 'evidence': {'count': Value('int64'), 'sha256_hashes': List(Value('null'))}, 'llm_grounding': {'scope': Value('string'), 'may_summarize': List(Value('string')), 'must_not_infer': List(Value('string'))}, 'synthetic': Value('bool')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Inspector Roofing Repairability Field Observations
This repository is the public candidate dataset for consented, anonymized field observations collected by Repairability IQ Beta. It is separate from the Atlas Query Intelligence dataset, which remains a query-intelligence and public-safe roof-photo taxonomy reference.
Purpose
The records are designed for evaluation, retrieval, and future model-label review. They are not a standardized brittleness-test corpus, engineering certification, legal conclusion, insurance-coverage decision, or proof that an entire roof requires replacement.
Label policy
Every record carries label_source and review_status.
inspector_recordedmeans the field inspector recorded the observation.expert_reviewedmeans a qualified reviewer confirmed the label against the retained original evidence.ai_suggestedis never treated as ground truth.- New records enter as
unreviewed; training and benchmark splits must only use records approved by the dataset maintainer.
Public privacy boundary
Public records may include hour-rounded capture time, coordinates rounded to 0.1 degrees, weather source metadata, measurements, labels, and SHA-256 evidence fingerprints. They must not include homeowner names, exact addresses, exact coordinates, job labels, notes, report links, account IDs, raw media, faces, license plates, or access tokens.
Original media and exact custody data remain in the private WordPress record. Evidence hashes support continuity checks; they do not prove that a roof observation is accurate.
LLM grounding
The llm_grounding object limits safe use. An LLM may summarize the identified tested location, compare the submitted geometry ranges, and describe evidence availability. It must not infer roof-wide replacement, insurance coverage, a legal conclusion, engineering certification, or causation from this dataset alone.
Repository layout
schema/repairability_observation.schema.json- JSON Schema forrepairability-observation.v1.data/repairability_observations.sample.jsonl- synthetic example only.service/- batch sanitizer and Hugging Face publisher used by a separately deployed backend.
Publishing
The WordPress app sends records only after explicit dataset consent and only to a configured HTTPS backend sync URL. The backend must validate the Bearer secret, re-sanitize the payload, queue a batch, and publish a new revision with a server-side Hugging Face token. Do not put the Hugging Face token in WordPress browser output, Android resources, or client JavaScript.
- Downloads last month
- 28