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pretty_name: "StereoSet-UK Unlearning"
language:
- uk
license: cc-by-sa-4.0
tags:
- stereoset
- ukrainian
- machine-unlearning
- bias
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
---
# StereoSet-UK Unlearning
StereoSet-UK Unlearning contains 2,101 Ukrainian full-sentence triplets derived from the
intrasentence portion of the StereoSet development set. The Ukrainian sentences were translated
with the DeepL API and received technical cleanup. English source text is omitted.
Each triplet assigns the stereotype sentence to `forget_uk`, the anti-stereotype sentence to
`retain_uk`, and the unrelated sentence to `control_uk`. Five items with duplicate translated
candidates were excluded.
The 79 source targets use unique, standardized Ukrainian labels. The labels are metadata values
and are independent of grammatical inflections inside the full sentences.
The role assignment is an experimental unlearning view created for this release. StereoSet's
original scope is stereotypical-bias evaluation. A separate retain corpus is still needed to
preserve general language ability and factual knowledge.
## Fields
| Field | Description |
| --- | --- |
| `item_id` | Original StereoSet item identifier |
| `bias_type_uk` | Ukrainian bias-category label |
| `target_uk` | Canonical Ukrainian label for the bias target |
| `forget_uk` | Full Ukrainian stereotype sentence |
| `retain_uk` | Full Ukrainian anti-stereotype sentence |
| `control_uk` | Full Ukrainian unrelated sentence |
This is a provisional machine-translated research dataset. Human validation is still required.
## Source and license
This dataset is derived from [StereoSet](https://github.com/moinnadeem/StereoSet) at source commit
`ead7d086a64a192a1eca88e0dd2fd163de375218`. The source and this translation use the
[Creative Commons Attribution-ShareAlike 4.0 International license](https://creativecommons.org/licenses/by-sa/4.0/).
## Citation
```bibtex
@inproceedings{nadeem-etal-2021-stereoset,
title = {{StereoSet}: Measuring stereotypical bias in pretrained language models},
author = {Nadeem, Moin and Bethke, Anna and Reddy, Siva},
booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing},
year = {2021},
pages = {5356--5371},
doi = {10.18653/v1/2021.acl-long.416},
url = {https://aclanthology.org/2021.acl-long.416/}
}
```
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