Create README.md

README.md

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+[hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) fine-tuned on the [CDConv dataset](https://github.com/thu-coai/cdconv). It supports 2-class classification for 2-turn dialogue contradiction detection. Usage example:
+
+```python
+import torch
+from transformers import AutoTokenizer
+from transformers.models.bert.modeling_bert import BertForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained('chujiezheng/roberta-base-cdconv')
+model = BertForSequenceClassification.from_pretrained('chujiezheng/roberta-base-cdconv')
+model.eval()
+
+turn1 = [
+    "嗯嗯，你喜欢钓鱼吗？", # user
+    "喜欢啊，钓鱼很好玩的", # bot
+]
+turn2 = [
+    "你喜欢钓鱼吗？", # user
+    "不喜欢，我喜欢看别人钓鱼", # bot, we want to identify whether this utterance makes a contradiction
+] # turn1 and turn2 are not required to be two consecutive turns
+text1 = "[SEP]".join(turn1 + turn2[:1])
+text2 = turn2[1]
+
+model_input = tokenizer(text1, text2, return_tensors='pt', return_token_type_ids=True, return_attention_mask=True)
+model_output = model(**model_input, return_dict=False)
+prediction = torch.argmax(model_output[0].cpu(), dim=-1)[0].item()
+print(prediction) # 0 for non-contradiction, 1 for contradiction
+```
+
+This fine-tuned model obtains 75.7 accuracy and 72.3 macro-F1 on the test set.
+
+Please kindly cite the [original paper](https://arxiv.org/abs/2210.08511) if you use this model.
+
+```bib
+@inproceedings{zheng-etal-2022-cdconv,
+  title={Towards Emotional Support Dialog Systems},
+  author={Zheng, Chujie  and 
+    Zhou, Jinfeng  and 
+    Zheng, Yinhe  and 
+    Peng, Libiao  and 
+    Guo, Zhen  and 
+    Wu, Wenquan  and 
+    Niu, Zhengyu  and 
+    Wu, Hua  and 
+    Huang, Minlie},
+  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
+  year={2022}
+}
+```