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.
🔬 Notes on Textile Image Text data
Overview
A documented Textile data-preparation workflow for Image Text records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus.
Included Material
build_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.
Data Sources
Source material is referenced but not bundled. Record the source version, retrieval date and any filtering rule so the same rows can be reproduced later.
Processing Choices
- Storage format: tfrecord
- Preprocessing: minimal
- Augmentation: mixup cutmix
- Split strategy: temporal
- Sampling: hard negative
- Quality checks: adaptive
- Labeling: self training
Risks And Limitations
External sources may be unrepresentative, mis-licensed, or contain sensitive fields. Source review, privacy checks and bias analysis are the user's responsibility before training or redistribution.
Intended Use
- pipeline testing and source adaptation
- controlled research split preparation
- not a training corpus — schema inspection only
Files
| File | Purpose |
|---|---|
build_dataset.py |
primary artifact |
README.md |
this documentation |
dataset_infos.json |
schema metadata |
metadata_sample.jsonl |
schema sample |
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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