The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: AttributeError
Message: 'str' object has no attribute 'items'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1215, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1190, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 700, in get_module
config_name: DatasetInfo.from_dict(dataset_info_dict)
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'items'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.
NLP QA Sensor Fusion Data Notes
Dataset summary
A documented NLP QA data-preparation workflow for Sensor Fusion records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Included material
dataset.py— loading, cleaning, and split preparation code.dataset_infos.json— schema and split metadata.metadata_sample.jsonl— small, human-readable records for checking the schema.README.md— data card and usage notes.
Processing choices
| Stage | Setting |
|---|---|
| Storage format | webdataset |
| Preprocessing | domain specific |
| Augmentation | mixup cutmix |
| Split strategy | stratified 90 10 |
| Sampling | active |
| Quality checks | strict |
| Labeling | pseudo label |
Validation checklist
Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows.
Intended use
The repository is suitable for testing the data pipeline, adapting it to a documented source, and preparing controlled research splits. Release status: metadata sample; full source data not bundled. The sample is for schema inspection only and should not be reported as a full training corpus.
Risks and limitations
The loader cannot guarantee that an external source is representative, correctly licensed, or free of sensitive information. Users remain responsible for source review, privacy checks, and bias analysis before training or redistribution.
Files
dataset.py— primary artifactREADME.md— this documentationdataset_infos.json— schema metadatametadata_sample.jsonl— schema sample
License
Released under apache-2.0. Review the source-data terms separately when this repository is used with external datasets.
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