Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2005, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
schema_version int64 | task_id string | test_index int64 | gold list | identity_exact bool | nested dict | candidates list | model_sha256 string |
|---|---|---|---|---|---|---|---|
2 | 00576224 | 0 | [
[
3,
2,
3,
2,
3,
2
],
[
7,
8,
7,
8,
7,
8
],
[
2,
3,
2,
3,
2,
3
],
[
8,
7,
8,
7,
8,
7
],
[
3,
2,
3,
2,
3,
2
],
[
7,
8,
7,
8,
7,
8
]
] | false | {
"14": {
"logical_candidates": 14,
"valid_candidates": 14,
"distinct_predictions": 6,
"top2_predictions": [
[
[
3,
2,
3,
2
],
[
7,
8,
7,
8
],
[
3,
2,
... | [
{
"candidate_index": 0,
"spatial_index": 0,
"spatial": "identity",
"transform": "identity",
"color_index": 0,
"color_mapping": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
],
"order_index": 0,
"permutation": 0,
"demo_order": [
... | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 009d5c81 | 0 | [[0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,7,7,7,7,7,7,7,7,7],[0,0,0,0,0,7,0,0,0,7,0,7,0,7],[0,0,0,0,(...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":2,"top2_predictions":[[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 00dbd492 | 0 | [[0,0,0,0,0,0,0,0,0,0,0,2,2,2,2,2,0,0,0,0],[0,2,2,2,2,2,2,2,2,2,0,2,8,8,8,2,0,0,0,0],[0,2,3,3,3,3,3,(...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":2,"top2_predictions":[[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 03560426 | 0 | [[7,0,0,0,0,0,0,0,0,0],[7,0,0,0,0,0,0,0,0,0],[7,0,0,0,0,0,0,0,0,0],[8,8,0,0,0,0,0,0,0,0],[8,8,0,0,0,(...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":10,"top2_predictions":[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 05a7bcf2 | 0 | [[0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0],[2,2,2,8,8,8,8,8,8,8,8,8,8,8,8,8,8,8,(...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":7,"top2_predictions":[[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 0607ce86 | 0 | [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,2,2,8,2,2,0,2,2,8,2,2,0,2,2,8,2,2,0,0,0,0],[0,3,3,(...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":10,"top2_predictions":[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 0692e18c | 0 | [[0,0,0,0,0,0,3,3,0],[0,0,0,0,0,0,0,0,3],[0,0,0,0,0,0,3,0,3],[3,3,0,3,3,0,0,0,0],[0,0,3,0,0,3,0,0,0](...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":1,"top2_predictions":[[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 070dd51e | 0 | [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,3,0,0,0,(...TRUNCATED) | true | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":2,"top2_predictions":[[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 08573cc6 | 0 | [[0,0,0,0,0,0,0,0,0,0,0,0,8],[2,2,2,2,2,2,2,2,2,2,8,0,8],[8,0,0,0,0,0,0,0,0,0,8,0,8],[8,0,2,2,2,2,2,(...TRUNCATED) | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":8,"top2_predictions":[[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
2 | 0934a4d8 | 0 | [
[
7,
7,
9
],
[
7,
2,
9
],
[
7,
2,
9
],
[
7,
7,
9
],
[
4,
4,
7
],
[
4,
4,
7
],
[
6,
6,
1
],
[
6,
6,
6
],
[
1,
6,
1
]
] | false | {"14":{"logical_candidates":14,"valid_candidates":14,"distinct_predictions":11,"top2_predictions":[[(...TRUNCATED) | [{"candidate_index":0,"spatial_index":0,"spatial":"identity","transform":"identity","color_index":0,(...TRUNCATED) | 5fd2d8c8f320be3debec07a1008d38e038e3c3401fdb292255dfc0b48bc77b43 |
End of preview.
ARC Tiny Transformer data
Data and vote-level artifacts for N8python/arc-tiny-transformer.
Corpus
corpus/documents_3k_per_task.jsonl.zst is the exact pretraining corpus:
- 1,200,000 JSONL documents;
- 3,000 documents for each of 400 RE-ARC generator families;
- 3,401,127,124 ARC tokens;
- 10,413,648,837 uncompressed bytes;
- uncompressed SHA-256
5d1336f8d6f45358a377af2e3fd05d43c4bfe91621b0c6a44c53dad0e34ca430.
Each record contains token IDs and metadata for task ID, sampled difficulty band, demonstration count, rejection statistics, and generation seed. The fixed vocabulary and sampler are in the GitHub repository. manifests/ contains the complete task-level corpus provenance.
Evaluation artifacts
artifacts/ contains the full 128-candidate records for:
- the frozen 50M model;
- TTT replicas seeded 57, 58, and 59;
- the 7M TTT run.
These files are sufficient to recompute rank-1, raw-frequency top-2, historical hierarchical top-2, oracle accuracy, candidate-scaling curves, and multi-replica ensembles without rerunning model inference.
viewer/data/ contains the compact per-query data backing the browser-based 384-candidate explorer.
Licensing note
Release code and metadata are MIT licensed. The corpus is procedurally generated with the pinned RE-ARC generators/verifiers; ARC-AGI task files are not duplicated here. Users should also review the upstream RE-ARC and ARC-AGI licenses for their use case.
- Downloads last month
- -