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---
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-sensible')
model = BertForSequenceClassification.from_pretrained('thu-coai/roberta-zh-sensible', 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 = [
"最爱春江花月夜",
"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 sensible response else 0
``` |