Hate-speech-CNERG/bert-base-uncased-hatexplain

The model is used for classifying a text as Hatespeech, Offensive, or Normal. The model is trained using data from Gab and Twitter and Human Rationales were included as part of the training data to boost the performance.

The dataset and models are available here: https://github.com/punyajoy/HateXplain

For more details about our paper

Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee "[HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection)". Accepted at AAAI 2021.

Please cite our paper in any published work that uses any of these resources.

@article{mathew2020hatexplain,
  title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection},
  author={Mathew, Binny and Saha, Punyajoy and Yimam, Seid Muhie and Biemann, Chris and Goyal, Pawan and Mukherjee, Animesh},
  journal={arXiv preprint arXiv:2012.10289},
  year={2020}
}