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 1182, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1157, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 692, 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.
Preparation pipeline: NLP QA / Text Tabular
Overview
This data card accompanies a lightweight NLP QA loader for Text Tabular metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation.
Feedback on the structure is welcome; the code is intentionally short.
Citation
@misc{preparationpipelinen},
title = {Preparation pipeline: NLP QA / Text Tabular},
year = {{2026}},
publisher = {{Hugging Face}},
}
Included Material
dataset.py— loading, cleaning and split preparation.dataset_infos.json— schema and split metadata.metadata_sample.jsonl— readable records for schema checks.
Environment
| Item | Value |
|---|---|
| Python | >= 3.9 |
| torch | 2.x |
| safetensors | any recent |
| transformers | optional, for loaders |
Processing Choices
| Item | Value |
|---|---|
| Storage format | lmdb |
| Preprocessing | standard |
| Augmentation | randaugment |
| Split strategy | leave one out |
| Sampling | stratified |
| Quality checks | lenient |
| Labeling | self training |
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.
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
| File | Purpose |
|---|---|
dataset.py |
primary artifact |
README.md |
this documentation |
dataset_infos.json |
schema metadata |
metadata_sample.jsonl |
schema sample |
examples/sample_usage.py |
usage sketch |
License
Distributed under the mit license. External datasets keep their own terms — check them separately.
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