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.
Ecommerce Audio Video Data Notes
Overview
This repository contains a preparation pipeline and a small metadata sample for Ecommerce work with Audio Video inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated.
Expect rough edges — this is a working sketch, not a polished release.
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.
Validation checklist
- check source licenses before redistribution
- scan for duplicates across splits
- inspect missing values and label balance
- watch for modality-specific corruption
Processing choices
- Storage format: parquet
- Preprocessing: standard
- Augmentation: light
- Split strategy: leave one out
- Sampling: balanced
- Quality checks: adaptive
- Labeling: pseudo label
Data sources
- external sources are referenced, not redistributed
- record retrieval date and filtering rules
- keep provenance explicit before any training run
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.
Changelog
- 2026-10-12 — initial upload
- 2026-10-21 — config and docs sync
Included material
loader.py— loading, cleaning and split preparation.dataset_infos.json— schema and split metadata.metadata_sample.jsonl— readable records for schema checks.
Files
| File | Purpose |
|---|---|
loader.py |
primary artifact |
README.md |
this documentation |
dataset_infos.json |
schema metadata |
metadata_sample.jsonl |
schema sample |
examples/sample_usage.py |
usage sketch |
License
Released under cc-by-nc-4.0. Review the source-data terms separately when this repository is used with external datasets.
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