Skip to content

datasets.md

Latest commit

 

History

History
77 lines (65 loc) · 3.84 KB

datasets.md

File metadata and controls

77 lines (65 loc) · 3.84 KB
Raw
Dataset License Link
Phishing Dataset MIT Hugging Face
Measuring Hate Speech CC-BY-4.0 Hugging Face
Tweet Eval (SemEval-2019) [See Citation]* Hugging Face
NSFW Detect MIT Hugging Face (Unused)
Toxic Chat CC-BY-NC-4.0 Hugging Face
Jigsaw Toxicity Apache-2.0 Hugging Face
Text Moderation Multilingual Apache-2.0 Hugging Face

Citation: ucberkeley-dlab/measuring-hate-speech

@article{kennedy2020constructing,
  title={Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application},
  author={Kennedy, Chris J and Bacon, Geoff and Sahn, Alexander and von Vacano, Claudia},
  journal={arXiv preprint arXiv:2009.10277},
  year={2020}
}

Citation: cardiffnlp/tweet_eval

@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel Pardo, Francisco Manuel and Rosso, Paolo and Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/S19-2007",
    doi = "10.18653/v1/S19-2007",
    pages = "54--63"
}

Citation: deepghs/nsfw_detect

@misc{nsfw_detect_dataset,
  title        = {NSFW Detection Dataset},
  author       = {deepghs},
  howpublished = {\url{https://huggingface.co/datasets/deepghs/nsfw_detect}},
  year         = {2023},
  note         = {Multi-class image classification dataset for NSFW content detection with five categories: drawing, hentai, neutral, porn, and sexy},
  abstract     = {This dataset is specifically designed for training NSFW (Not Safe For Work) detection models in the context of artistic content and image classification. The collection follows the established categorization format from popular NSFW detection implementations, providing a comprehensive benchmark for content moderation systems. The dataset contains images organized into five distinct classes that represent different levels of appropriateness and content types commonly encountered in online platforms. The classification framework divides images into drawing, hentai, neutral, porn, and sexy categories, enabling models to distinguish between various types of potentially sensitive content with fine-grained precision.},
  keywords     = {NSFW detection, image classification, content moderation, artistic content, multi-class categorization}
}

Citation: lmsys/toxic-chat

@misc{lin2023toxicchat,
      title={ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation}, 
      author={Zi Lin and Zihan Wang and Yongqi Tong and Yangkun Wang and Yuxin Guo and Yujia Wang and Jingbo Shang},
      year={2023},
      eprint={2310.17389},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Citation: KoalaAI/Text-Moderation-Multilingual

@misc{text-moderation-large,
  title={Text-Moderation-Multilingual: A Multilingual Text Moderation Dataset},
  author={[KoalaAI]},
  year={2025},
  note={Aggregated from ifmain's and OpenAI's moderation datasets}
}