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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Float value 16.200000 was truncated converting to int64
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, 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 1861, 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 2068, 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 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, 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 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, 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 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, 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 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, 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 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, 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 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2015, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1161, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 414, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Float value 16.200000 was truncated converting to int64
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 1694, 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 1880, 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.
annotations dict | id string | meta dict | parents list | payload dict | provenance dict | schema_version string |
|---|---|---|---|---|---|---|
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"lineage_id": "libretexts:6ca7667ad7b1748516a0638247f94054deb6faf2a8bf9ec7b5cdd722d2e30cf3",
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"purpose": "explain",
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},
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"content_sha256": "d9f574a21372d2bb47e9be11dfdcc9a9782dd76def3a6f41470c843707ff6f79",
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"physical_locator": {
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{"artifact_design_language":{"em_dash_count":0,"em_dashes_per_1000_visible_characters":0.0,"generic_(...TRUNCATED) | artifact_00048ebd662f9157730d606e | {"artifact_type":"slides","cluster":"artifacts","deck_arc":"context_to_options_to_recommendation","l(...TRUNCATED) | [
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{"artifact_design_language":{"em_dash_count":0,"em_dashes_per_1000_visible_characters":0.0,"generic_(...TRUNCATED) | artifact_002c8334f8448f8f6b9363c0 | {"artifact_type":"slides","cluster":"artifacts","deck_arc":"question_to_mechanism_to_implication","l(...TRUNCATED) | [
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{"artifact_design_language":{"em_dash_count":0,"em_dashes_per_1000_visible_characters":0.0,"generic_(...TRUNCATED) | artifact_002f7fcafb4da6eb5ada7dd8 | {"artifact_type":"slides","cluster":"artifacts","deck_arc":"criteria_to_tradeoffs_to_decision","line(...TRUNCATED) | [
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{"artifact_design_language":{"em_dash_count":0,"em_dashes_per_1000_visible_characters":0.0,"generic_(...TRUNCATED) | artifact_0031e5e517c4c1e22d6d4331 | {"artifact_type":"slides","cluster":"artifacts","deck_arc":"criteria_to_tradeoffs_to_decision","line(...TRUNCATED) | [
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End of preview.
Placeholder Labs Artifacts Pool
Private, source-grounded corpus of complete, editable, self-contained HTML slide decks. The current production release contains 9,066 accepted canonical decks, 34,601,632 unique Dolma-2 HTML tokens, and two linked training views per deck.
canonical: natural request, complete HTML, source lineage, design assignment, teacher evidence, and static/browser validation evidence;annealing_html: raw complete HTML without chat or factory wrappers;sft_create: natural user request followed by the same complete HTML assistant answer.
The two training views are serializations of one accepted artifact, not independent content. Their combined 73,989,907 message-content tokens repeat the HTML once. Factory source context, validation ledgers, design assignments, and acceptance instructions are excluded from training payloads.
Every accepted deck passed pinned-browser rendering, minimum keyboard navigation, print exposure, clipping, text-size, console, network, fact-preservation, and anti-pattern checks. The release contains 9,066 unique source lineages and HTML hashes, all 30 scheduled visual systems, and no detected first-slide or whole-deck perceptual near duplicate at the release threshold.
Source rows retain per-record provenance and rights evidence. Because the corpus is a mixture of public-domain, Creative Commons, Apache-licensed, and internally owned source derivatives, there is no single blanket corpus licence.
Rendered screenshots are content-addressed evidence in private S3 rather than duplicated inside training JSONL. Canonical rows retain the immutable screenshot hash and locator.
See RELEASE.json, schema/phase5-10000-v1/, and
receipts/phase5-10000-v1/ for release counts, schema, publication, and migration
evidence.
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