UkrTweetToxicityBinaryClassification_ukr-detect_ukr-toxicity-dataset

An MTEB dataset

Massive Text Embedding Benchmark

Filtered Ukrainian Tweets dataset with Toloka.ai platform crowdsourcing task. It contains of 2.5k toxic and 2.5k non-toxic texts.

Task category	t2c
Domains	Blog, Social, Web
Reference	https://huggingface.co/datasets/ukr-detect/ukr-toxicity-dataset

Source datasets:

• ukr-detect/ukr-toxicity-dataset

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_task("UkrTweetToxicityBinaryClassification_ukr-detect_ukr-toxicity-dataset")
evaluator = mteb.MTEB([task])

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repository.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.

@inproceedings{dementieva-etal-2024-toxicity,
    title = "Toxicity Classification in {U}krainian",
    author = "Dementieva, Daryna  and
      Khylenko, Valeriia  and
      Babakov, Nikolay  and
      Groh, Georg",
    editor = {Chung, Yi-Ling  and
      Talat, Zeerak  and
      Nozza, Debora  and
      Plaza-del-Arco, Flor Miriam  and
      R{"o}ttger, Paul  and
      Mostafazadeh Davani, Aida  and
      Calabrese, Agostina},
    booktitle = "Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.woah-1.19",
    doi = "10.18653/v1/2024.woah-1.19",
    pages = "244--255",
    abstract = "The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora are not available for all languages, which is understandable given the resource-intensive nature of the annotation process. Ukrainian, in particular, is among the languages lacking such resources. To our knowledge, there has been no existing toxicity classification corpus in Ukrainian. In this study, we aim to fill this gap by investigating cross-lingual knowledge transfer techniques and creating labeled corpora by: (i){	extasciitilde}translating from an English corpus, (ii){	extasciitilde}filtering toxic samples using keywords, and (iii){	extasciitilde}annotating with crowdsourcing. We compare LLMs prompting and other cross-lingual transfer approaches with and without fine-tuning offering insights into the most robust and efficient baselines.",
}


@article{enevoldsen2025mmtebmassivemultilingualtext,
  title={MMTEB: Massive Multilingual Text Embedding Benchmark},
  author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2502.13595},
  year={2025},
  url={https://arxiv.org/abs/2502.13595},
  doi = {10.48550/arXiv.2502.13595},
}

@article{muennighoff2022mteb,
  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
  title = {MTEB: Massive Text Embedding Benchmark},
  publisher = {arXiv},
  journal={arXiv preprint arXiv:2210.07316},
  year = {2022}
  url = {https://arxiv.org/abs/2210.07316},
  doi = {10.48550/ARXIV.2210.07316},
}

Dataset Statistics

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("UkrTweetToxicityBinaryClassification_ukr-detect_ukr-toxicity-dataset")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 2000,
        "number_texts_intersect_with_train": 0,
        "text_statistics": {
            "total_text_length": 124693,
            "min_text_length": 13,
            "average_text_length": 62.3465,
            "max_text_length": 989,
            "unique_texts": 2000
        },
        "image_statistics": null,
        "label_statistics": {
            "min_labels_per_text": 1,
            "average_label_per_text": 1.0,
            "max_labels_per_text": 1,
            "unique_labels": 2,
            "labels": {
                "0": {
                    "count": 1000
                },
                "1": {
                    "count": 1000
                }
            }
        }
    },
    "train": {
        "num_samples": 2998,
        "number_texts_intersect_with_train": null,
        "text_statistics": {
            "total_text_length": 193627,
            "min_text_length": 16,
            "average_text_length": 64.58539026017345,
            "max_text_length": 2798,
            "unique_texts": 2998
        },
        "image_statistics": null,
        "label_statistics": {
            "min_labels_per_text": 1,
            "average_label_per_text": 1.0,
            "max_labels_per_text": 1,
            "unique_labels": 2,
            "labels": {
                "1": {
                    "count": 1499
                },
                "0": {
                    "count": 1499
                }
            }
        }
    }
}

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This dataset card was automatically generated using MTEB