Datasets:
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
cachePolicy: struct<invalidationRule: string, mode: string, recommendedKeyFields: list<item: string>>
child 0, invalidationRule: string
child 1, mode: string
child 2, recommendedKeyFields: list<item: string>
child 0, item: string
capabilities: struct<fullReasoningSafe: bool, headerProbe: bool, nodeRouteIndex: bool, selectedChunkRead: bool, sm (... 31 chars omitted)
child 0, fullReasoningSafe: bool
child 1, headerProbe: bool
child 2, nodeRouteIndex: bool
child 3, selectedChunkRead: bool
child 4, smallNeighborhoodExpansion: bool
createdAtUtc: timestamp[s]
hfObjects: struct<index: struct<mediaType: string, path: string, role: string, sizeBytes: int64>, left: struct< (... 388 chars omitted)
child 0, index: struct<mediaType: string, path: string, role: string, sizeBytes: int64>
child 0, mediaType: string
child 1, path: string
child 2, role: string
child 3, sizeBytes: int64
child 1, left: struct<count: int64, mediaType: string, pathPrefix: string, role: string>
child 0, count: int64
child 1, mediaType: string
child 2, pathPrefix: string
child 3, role: string
child 2, manifest: struct<mediaType: string, path: string, role: string>
child 0, mediaType: string
child 1, path: string
child 2, role: string
child 3, nameOfNode: struct<count: int64, mediaType: string, pathPrefix: string, role: string>
child 0, count: int64
child 1, mediaType: string
child 2, pathPrefix: string
...
g
child 4, totalChunkBytes: int64
child 5, totalChunkCount: int64
storageMode: string
transport: struct<fallback: string, primary: string>
child 0, fallback: string
child 1, primary: string
nodeOfName: list<item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, lang: string
nameOfNode: list<item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, lang: string
left: list<item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, which: string
file: string
header: struct<offset: int64, length: int64>
child 0, offset: int64
child 1, length: int64
right: list<item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, which: string
to
{'file': Value('string'), 'header': {'offset': Value('int64'), 'length': Value('int64')}, 'left': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'which': Value('string')}), 'right': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'which': Value('string')}), 'nameOfNode': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'lang': Value('string')}), 'nodeOfName': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'lang': 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 478, 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
cachePolicy: struct<invalidationRule: string, mode: string, recommendedKeyFields: list<item: string>>
child 0, invalidationRule: string
child 1, mode: string
child 2, recommendedKeyFields: list<item: string>
child 0, item: string
capabilities: struct<fullReasoningSafe: bool, headerProbe: bool, nodeRouteIndex: bool, selectedChunkRead: bool, sm (... 31 chars omitted)
child 0, fullReasoningSafe: bool
child 1, headerProbe: bool
child 2, nodeRouteIndex: bool
child 3, selectedChunkRead: bool
child 4, smallNeighborhoodExpansion: bool
createdAtUtc: timestamp[s]
hfObjects: struct<index: struct<mediaType: string, path: string, role: string, sizeBytes: int64>, left: struct< (... 388 chars omitted)
child 0, index: struct<mediaType: string, path: string, role: string, sizeBytes: int64>
child 0, mediaType: string
child 1, path: string
child 2, role: string
child 3, sizeBytes: int64
child 1, left: struct<count: int64, mediaType: string, pathPrefix: string, role: string>
child 0, count: int64
child 1, mediaType: string
child 2, pathPrefix: string
child 3, role: string
child 2, manifest: struct<mediaType: string, path: string, role: string>
child 0, mediaType: string
child 1, path: string
child 2, role: string
child 3, nameOfNode: struct<count: int64, mediaType: string, pathPrefix: string, role: string>
child 0, count: int64
child 1, mediaType: string
child 2, pathPrefix: string
...
g
child 4, totalChunkBytes: int64
child 5, totalChunkCount: int64
storageMode: string
transport: struct<fallback: string, primary: string>
child 0, fallback: string
child 1, primary: string
nodeOfName: list<item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, lang: string
nameOfNode: list<item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, lang: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, lang: string
left: list<item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, which: string
file: string
header: struct<offset: int64, length: int64>
child 0, offset: int64
child 1, length: int64
right: list<item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>>
child 0, item: struct<chunkIndex: int64, offset: int64, length: int64, which: string>
child 0, chunkIndex: int64
child 1, offset: int64
child 2, length: int64
child 3, which: string
to
{'file': Value('string'), 'header': {'offset': Value('int64'), 'length': Value('int64')}, 'left': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'which': Value('string')}), 'right': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'which': Value('string')}), 'nameOfNode': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'lang': Value('string')}), 'nodeOfName': List({'chunkIndex': Value('int64'), 'offset': Value('int64'), 'length': Value('int64'), 'lang': Value('string')})}
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.
zelph Binaries Dataset
This dataset provides pre-compiled binary files (.bin) for use with zelph, a sophisticated semantic network system. These binaries are derived from large knowledge bases like Wikidata, optimized for fast loading and efficient querying.
Dataset Description
zelph binaries enable users to work with semantic networks without the need to import raw dumps (e.g., JSON files), which can take hours. Instead, these .bin files load in minutes, though they require substantial RAM.
The dataset includes both full and pruned variants:
- Full binaries: Contain the complete network, suitable for comprehensive use but demanding high RAM (Wikidata: ~ 210 GB RAM).
- Pruned binaries: Reduced versions with removed domains (e.g., biology, chemistry, astronomy) to lower RAM requirements (~ 16 GB RAM) while preserving core connections.
For detailed information on each binary, including sizes, creation dates, pruning details, and updates, visit https://zelph.org/binaries.
How to Use
- Download the desired .bin file from this dataset.
- In zelph interactive mode, load it with:
.load /path/to/your-file.bin - Run queries, define rules, perform inferences or run complete scripts (see zelph on GitHub for details).
LLM-Friendly Outputs
zelph can generate rule-based inferences in a compressed text format optimized for LLM training or processing. This uses a token encoder that maps Wikidata IDs (Q/P) to compact UTF-8 symbols (CJK range), reducing input length while preserving structure.
This feature is currently focused on Wikidata, but it can be adapted for similar use cases. Use it to export inferences from loaded binaries for LLM datasets – see the command documentation on GitHub for details.
Citation
If you use this dataset, cite as:
@dataset{zelph,
author = {Stefan Zipproth},
title = {zelph Binaries Dataset},
year = {2026},
url = {https://huggingface.co/datasets/acrion/zelph}
}
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