The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
version: string
sample_id: string
model: string
provider: string
ablation: string
response: string
error: string
schema_valid_rate: double
topology_equivalent_accuracy: double
skill_exact_accuracy: double
gold_rows: int64
matched_prediction_rows: int64
strict_continuation_success_rate: double
strict_continuation_trajectories: int64
protocol: string
prediction_rows: int64
strict_json_rate: double
skill_vocabulary_compliance_rate: double
semantic_exact_accuracy: double
strict_continuation_successes: int64
recoverable_json_rate: double
arguments_exact_accuracy: double
to
{'version': Value('string'), 'protocol': Value('string'), 'gold_rows': Value('int64'), 'prediction_rows': Value('int64'), 'matched_prediction_rows': Value('int64'), 'strict_json_rate': Value('float64'), 'recoverable_json_rate': Value('float64'), 'schema_valid_rate': Value('float64'), 'skill_vocabulary_compliance_rate': Value('float64'), 'skill_exact_accuracy': Value('float64'), 'arguments_exact_accuracy': Value('float64'), 'semantic_exact_accuracy': Value('float64'), 'topology_equivalent_accuracy': Value('float64'), 'strict_continuation_successes': Value('int64'), 'strict_continuation_trajectories': Value('int64'), 'strict_continuation_success_rate': Value('float64')}
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
version: string
sample_id: string
model: string
provider: string
ablation: string
response: string
error: string
schema_valid_rate: double
topology_equivalent_accuracy: double
skill_exact_accuracy: double
gold_rows: int64
matched_prediction_rows: int64
strict_continuation_success_rate: double
strict_continuation_trajectories: int64
protocol: string
prediction_rows: int64
strict_json_rate: double
skill_vocabulary_compliance_rate: double
semantic_exact_accuracy: double
strict_continuation_successes: int64
recoverable_json_rate: double
arguments_exact_accuracy: double
to
{'version': Value('string'), 'protocol': Value('string'), 'gold_rows': Value('int64'), 'prediction_rows': Value('int64'), 'matched_prediction_rows': Value('int64'), 'strict_json_rate': Value('float64'), 'recoverable_json_rate': Value('float64'), 'schema_valid_rate': Value('float64'), 'skill_vocabulary_compliance_rate': Value('float64'), 'skill_exact_accuracy': Value('float64'), 'arguments_exact_accuracy': Value('float64'), 'semantic_exact_accuracy': Value('float64'), 'topology_equivalent_accuracy': Value('float64'), 'strict_continuation_successes': Value('int64'), 'strict_continuation_trajectories': Value('int64'), 'strict_continuation_success_rate': Value('float64')}
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.
AI4EDA: local release candidate
AI4EDA contains KiCad GUI observations and action records, plus schematic files and a visual-skill smoke subset. This package preserves source records and is prepared for review; inclusion does not establish that every record is suitable for training or redistribution.
Contents
| Component | Records | Description |
|---|---|---|
annotations/gui_events.jsonl.gz |
6,832 | Non-quarantined GUI action events, including human and teacher-augmented sources |
annotations/precision_events.jsonl.gz |
2,892 | Events from 50 sessions selected by the existing Precision manifest |
annotations/precision_schematics.jsonl.gz |
50 | Session-to-final-schematic mappings; final schematics are not intermediate step states |
annotations/oracle_schematics.jsonl.gz |
103 | Instruction-to-schematic records; 98 source records have missing or unresolved files |
annotations/schematic_inventory.jsonl.gz |
1,850 | Schematic candidate paths, source group, format, size and SHA-256; includes duplicates, external files, five Eagle files and four unknown/invalid files |
images/gui-events.tar, images/precision-events.tar |
19,448 images total | Before and after screenshots referenced by GUI event annotations |
schematics/schematic-candidates.tar |
1,850 files | Files in the candidate inventory, preserving relative paths |
subsets/oracle-visual-skill-smoke.tar |
504 records | Existing 372 train and 132 eval records with screenshots and source/target schematics; not independent of other collections |
metadata/image_manifest.jsonl.gz |
19,448 | Image path, archive, size and SHA-256 |
metadata/non_kicad_windows.jsonl |
31 | GUI events whose recorded active application is not KiCad, for review |
GUI rows contain instruction, session and task identifiers, before/after screenshot paths, event type, original action parameters and source type. Screen and window coordinates are distinct; window coordinates correspond to window screenshots. The event sets are not split into training and test data and may share tasks or content. The historical Oracle subset split is retained without a new generalization claim.
Reconstruct locally
Extract the two image archives and schematic archive into the same directory. Annotation paths beginning images/ and 原理图候选/ are relative to that directory. The Oracle archive extracts into 原理图与截图配对_Oracle/ and its own annotation paths are relative to that directory.
mkdir -p AI4EDA_data
tar -xf images/gui-events.tar -C AI4EDA_data
tar -xf images/precision-events.tar -C AI4EDA_data
tar -xf schematics/schematic-candidates.tar -C AI4EDA_data
tar -xf subsets/oracle-visual-skill-smoke.tar -C AI4EDA_data
SHA256SUMS verifies the packaged files. metadata/image_manifest.jsonl.gz describes the images inside the archives. JSONL annotations are gzip compressed and retain their original fields.
Limitations and provenance
The data combines recorded and augmented GUI trajectories, project-generated schematics, and files derived from or associated with external projects. The schematic inventory includes materials with differing or unverified licenses; no blanket license is asserted. Images, action text, window titles, external-source rights, and recording consent require release review. The 31 non-KiCad-window records are retained for completeness and explicitly indexed. Prior experiment output directories and sessions_1000_v2 were not recovered. The four unknown/invalid schematic candidates are kept and identified in the inventory; do not treat them as valid circuits. Counts refer to records or file paths, not necessarily independent tasks or circuits.
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