readme: ---
  language:
  - zh
  tags:
  - pytorch
  - zh
  - Conversational
  
  ---
  
  [roberta-zh](https://github.com/brightmart/roberta_zh) fine-tuned on human-annotated conversational model self-chat data. It supports 2-class calssification for multi-turn dialogue sensible detection.
  Usage example:
  
  NOTE: it should be used under similar data distribution.
  
  ```python
  import torch
  from transformers import BertTokenizer, BertForSequenceClassification
  
  tokenizer = BertTokenizer.from_pretrained('thu-coai/roberta-zh-specific')
  model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-zh-specific', num_labels=2)
  model.eva()
  
  context = [
      "你大爱的冷门古诗词是什么?\t一枝红艳露凝香,云雨巫山枉断肠",
      "你大爱的冷门古诗词是什么?\t一枝红艳露凝香,云雨巫山枉断肠",
  ]
  
  response = [
      "我也很喜欢，我觉得这句的意境很美",
      "我也很喜欢",
  ]
  
  model_input = tokenizer(context, response, return_tensors='pt', padding=True)
  with torch.no_grad():
      model_output = model(**model_input, return_dict=True)
  logits = model_output.logits
  preds_all = torch.argmax(logits, dim=-1).cpu()
  print(preds_all) # 1 for specific response else 0
  
  
  ```