Create README.md

README.md

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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-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
+
+
+```