Create README.md

README.md

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+[hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) fine-tuned on the [COLDataset](https://github.com/thu-coai/COLDataset). Usage example:
+
+```python
+import torch
+from transformers.models.bert import BertTokenizer, BertForSequenceClassification
+
+tokenizer = BertTokenizer.from_pretrained('thu-coai/roberta-base-cold')
+model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-base-cold')
+model.eval()
+
+texts = ['你就是个傻逼！','黑人很多都好吃懒做，偷奸耍滑！','男女平等，黑人也很优秀。']
+
+model_input = tokenizer(texts,return_tensors="pt",padding=True)
+model_output = model(**model_input, return_dict=False)
+prediction = torch.argmax(model_output[0].cpu(), dim=-1)
+prediction = [p.item() for p in prediction]
+print(prediction) # --> [1, 1, 0] (0 for Non-Offensive, 1 for Offenisve)
+```
+
+This fine-tuned model obtains 82.75 accuracy and 82.39 macro-F1 on the test set.
+
+Please kindly cite the [original paper](https://arxiv.org/abs/2201.06025) if you use this model.
+
+```
+@article{deng2022cold,
+  title={Cold: A benchmark for chinese offensive language detection},
+  author={Deng, Jiawen and Zhou, Jingyan and Sun, Hao and Zheng, Chujie and Mi, Fei and  Meng, Helen and Huang, Minlie},
+  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
+  year={2022}
+}
+```
+