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  # Toxic language detection
  
  ## Model description
  
  A toxic language detection model trained on tweets. The base model is Roberta-large. For more information, 
  including the **training data**, **limitations and bias**, please refer to the [paper](https://arxiv.org/pdf/2102.00086.pdf) and
  Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
  
  #### How to use
  Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.
  
  ```python
  from transformers import pipeline
  classifier = pipeline("text-classification",model='Xuhui/ToxDect-roberta-large', return_all_scores=True)
  prediction = classifier("You are f**king stupid!", )
  print(prediction)
  
  """
  Output:
  [[{'label': 'LABEL_0', 'score': 0.002632011892274022}, {'label': 'LABEL_1', 'score': 0.9973680377006531}]]
  """
  ```
  
  ## Training procedure
  The random seed for this model is 22. For other details, please refer to the Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{zhou-etal-2020-debiasing,
    title = {Challenges in Automated Debiasing for Toxic Language Detection},
    author = {Zhou, Xuhui and Sap, Maarten and Swayamdipta, Swabha and Choi, Yejin and Smith, Noah A.},
    booktitle = {EACL},
    abbr = {EACL},
    html = {https://www.aclweb.org/anthology/2021.eacl-main.274.pdf},
    code = {https://github.com/XuhuiZhou/Toxic_Debias},
    year = {2021},
    bibtex_show = {true},
    selected = {true}
  }
  ```