Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
StopIteration
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/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DGUI-HyperMem Embedding Corpus
Embedding-model-format corpus for training / evaluating embedding and retrieval models from DGUI-HyperMem memories. Companion to the JEV training dataset ctaxnagomi/DGUI_HYPERMEM-JEV.
Schema-complete and currently EMPTY — rows appear once the ingest pipeline (spec: RULESET_TRAIN_CORPUS.md in the source repo) ships. The row format below is the contract it will be filled against.
Files
| File | Content |
|---|---|
corpus.jsonl |
one row per memory document (text + metadata + optional 768-d embedding) |
queries.jsonl |
one row per observed search query that returned results |
train.jsonl |
contrastive pairs: {query_id, doc_id, label} (1 = answer, 0 = captured-but-not-selected) |
metadata.json |
corpus stats: totals, by tag_id, by memory_type, embedding info, ingest timestamps |
corpus.jsonl row
{"doc_id": "mem_<uuid>", "text": "<scrubbed memory content>",
"metadata": {"tag_id": "u_<hex>", "scope": "default", "memory_type": "fact",
"source": null, "created_at": 1234567890000, "status": "active"},
"embedding": [0.018, "... 768 floats"]} // optional; @cf/baai/bge-base-en-v1.5
Governance (air-gapped)
- Redaction gate — every row passes the fail-closed scrub before ingestion (secrets, emails, env-var secret names barred).
- tagID required — rows without a non-null
metadata.tag_idare ineligible.tag_idis the truncated SHA-256 of the token owner's email (u_<hex>); it is attribution, never PII. - Write-only — the corpus is a sink. Nothing is ever pulled back from it.
- Single writer — the production Worker is the only writer.
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