Create README.md

README.md

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+datasets:
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+---
+
+# Toxic language detection
+
+## Model description
+
+A toxic language detection model trained on tweeter data. 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}
+}
+```