---
language:
- en
tags:
- text-classification
- human-vs-machine
- synthetic-data
- data-quality
task_categories:
- text-classification
pretty_name: QualiText
size_categories:
- 100K<n<1M
license: cc0-1.0
---

# 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 modified with deterministic word deletions, swaps, duplications, or typo-like edits. |
| `marketing` | Marketing-oriented text from FineWeb-Marketing. |
| `low_quality` | Spam, ham/phishing-email corpus examples, and synthetic text created by removing punctuation, lowercasing, and shuffling words. |

## Sources

The source datasets are:

- [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia)
- [`fuzzy-g/4chan_pol_whole_ds`](https://huggingface.co/datasets/fuzzy-g/4chan_pol_whole_ds)
- [`r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation`](https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
- [`marketeam/FineWeb-Marketing`](https://huggingface.co/datasets/marketeam/FineWeb-Marketing)
- [`seuun/spam-ham-phish-emails-latest`](https://huggingface.co/datasets/seuun/spam-ham-phish-emails-latest)

## 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.