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akaruineko/qualitext

QualiText QualiText is a balanced English text-classification dataset for studying text origin and text quality. Each example contains a text field and a label field. The dataset has five labels with the same number of examples in each class. Labels Label Description human Human-authored text from Wikipedia and 4chan. machine_generated Machine-generated text from the Qwen3.8-Max, GLM-5.2, and Kimi-K3 distillation corpus. corrupted Wikipedia text… See the full description on the dataset page: https://huggingface.co/datasets/akaruineko/qualitext.

sourceHugging Facecc0-1.0updated 1mo agoView on Hugging Face
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QualiText

QualiText is a balanced English text-classification dataset for studying text origin and text quality. Each example contains a text field and a label field. The dataset has five labels with the same number of examples in each class.

Labels

LabelDescription
humanHuman-authored text from Wikipedia and 4chan.
machine_generatedMachine-generated text from the Qwen3.8-Max, GLM-5.2, and Kimi-K3 distillation corpus.
corruptedWikipedia text modified with deterministic word deletions, swaps, duplications, or typo-like edits.
marketingMarketing-oriented text from FineWeb-Marketing.
low_qualitySpam, ham/phishing-email corpus examples, and synthetic text created by removing punctuation, lowercasing, and shuffling words.

Sources

The source datasets are:

Data Format

text
text: string
label: ClassLabel or string

Example:

json
{
  "text": "Example document text.",
  "label": "human"
}

Intended Uses

QualiText can be used for:

  • —Training baseline text-origin classifiers
  • —Evaluating robustness to common text corruption
  • —Comparing text-quality classification strategies
  • —Prototyping data filtering and moderation models

It should not be used as the sole basis for deciding whether a person used an AI system or whether content is trustworthy.

Limitations and Biases

  • —The labels represent dataset provenance and synthetic transformations, not definitive proof of authorship.
  • —The human class contains platform-specific and encyclopedic writing styles.
  • —The marketing and low-quality classes may contain strong lexical shortcuts.
  • —Synthetic corruption does not represent every real-world form of corruption.
  • —The machine-generated class may reflect the style and artifacts of its teacher models and prompts.
  • —Source datasets may contain offensive, private, copyrighted, or otherwise sensitive material. Review examples before deployment.
  • —Performance may not generalize to languages, domains, or writing styles not represented in the sources.

Licensing

QualiText is an aggregated dataset. The applicable license and usage terms of each source dataset may differ and continue to apply to the corresponding examples. Users are responsible for reviewing the source licenses and meeting their attribution, privacy, copyright, and acceptable-use obligations.

Citation

If you use QualiText, cite the dataset repository where it is published and also acknowledge the source datasets listed above.