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Datasets:
gauravmalho
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image-depth-samples

Tags:
dataset
legal
image-depth
metadata-sample
License:
Dataset card Files
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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 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'

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Preparation pipeline: Legal / Image Depth

Overview

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

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.

Included Material

  1. prepare.py — loading, cleaning, and split preparation code.
  2. dataset_infos.json — schema and split metadata.
  3. metadata_sample.jsonl — small, human-readable records for checking the schema.
  4. README.md — data card and usage notes.

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.

Acknowledgments

This repo borrows ideas freely from public reference implementations; credit to their authors.

Validation Checklist

  1. check source licenses before redistribution
  2. scan for duplicates across splits
  3. inspect missing values and label balance
  4. watch for modality-specific corruption

Citation 

@misc{preparationpipelinel},
  title = {Preparation pipeline: Legal / Image Depth},
  year = {{2026}},
  publisher = {{Hugging Face}},
}

Processing Choices

  1. Storage format: jsonl
  2. Preprocessing: aggressive
  3. Augmentation: none
  4. Split strategy: kfold 5
  5. Sampling: balanced
  6. Quality checks: strict
  7. Labeling: self training

Files

File Purpose
prepare.py primary artifact
README.md this documentation
dataset_infos.json schema metadata
metadata_sample.jsonl schema sample

License

Distributed under the cc0-1.0 license. External datasets keep their own terms — check them separately.
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