Datasets:

Atharv-kumar
/

memes-image-depth

Tags:

dataset

memes

image-depth

License:

Dataset card Files Files and versions

xet

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Dataset Viewer

The dataset viewer is not available for this dataset.

Cannot get the config names for the 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'

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Memes Image Depth Data Notes

Dataset summary

Preparation notes and schema examples for Memes tasks using Image Depth data. Full source material is intentionally not bundled, so provenance and licensing remain explicit.

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.

Processing choices

Stage	Setting
Storage format	csv
Preprocessing	progressive
Augmentation	none
Split strategy	stratified 90 10
Sampling	hard negative
Quality checks	strict
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.

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

build_dataset.py — primary artifact
README.md — this documentation
dataset_infos.json — schema metadata
metadata_sample.jsonl — schema sample

License

Released under mit. Review the source-data terms separately when this repository is used with external datasets.

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