thu-coai/roberta-base-cold

Ejemplo de uso:

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 para No Ofensivo, 1 para Ofensivo)

Por favor, cite el artículo original si utiliza este modelo:

@article{deng2022cold,
title={Cold: A benchmark for chinese offensive language detection},
author={Deng, Jiawen y Zhou, Jingyan y Sun, Hao y Zheng, Chujie y Mi, Fei y Meng, Helen y Huang, Minlie},
booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
year={2022}
}