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---
license: openrail++
dataset_info:
features:
- name: text
dtype: string
- name: tags
dtype: float64
splits:
- name: train
num_bytes: 2105604
num_examples: 12682
- name: validation
num_bytes: 705759
num_examples: 4227
- name: test
num_bytes: 710408
num_examples: 4214
download_size: 2073133
dataset_size: 3521771
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
## Ukrainian Toxicity Dataset
This is the first of its kind toxicity classification dataset for the Ukrainian language. The datasets was obtained semi-automatically by toxic keywords filtering. For manually collected datasets with crowdsourcing, please, check [textdetox/multilingual_toxicity_dataset](https://huggingface.co/datasets/textdetox/multilingual_toxicity_dataset).
Due to the subjective nature of toxicity, definitions of toxic language will vary. We include items that are commonly referred to as vulgar or profane language. ([NLLB paper](https://arxiv.org/pdf/2207.04672.pdf))
## Dataset formation:
1. Filtering Ukrainian tweets so that only tweets containing toxic language remain with toxic keywords. Source data: https://github.com/saganoren/ukr-twi-corpus
2. Non-toxic sentences were obtained from a previous dataset of tweets as well as sentences from news and fiction from UD Ukrainian IU: https://universaldependencies.org/treebanks/uk_iu/index.html
3. After that, the dataset was split into a train-test-val and all data were balanced both by the toxic/non-toxic criterion and by data source.
Labels: 0 - non-toxic, 1 - toxic.
## Load dataset:
```
from datasets import load_dataset
dataset = load_dataset("ukr-detect/ukr-toxicity-dataset")
```
## Citation
```
@article{dementieva2024toxicity,
title={Toxicity Classification in Ukrainian},
author={Dementieva, Daryna and Khylenko, Valeriia and Babakov, Nikolay and Groh, Georg},
journal={arXiv preprint arXiv:2404.17841},
year={2024}
}
``` |