hfl/chinese-roberta-wwm-ext fine-tuned on the COLDataset. Usage example:

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 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}
}