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README.md

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+This model has been trained on massive Chinese plain-text open-domain dialogues following the approach described in [Re$^3$Dial: Retrieve, Reorganize and Rescale Conversations for Long-Turn Open-Domain Dialogue Pre-training](https://arxiv.org/abs/2305.02606). The associated Github repository is available here https://github.com/thu-coai/Re3Dial.
+
+### Usage
+
+```python
+from transformers import BertTokenizer, BertModel
+import torch
+
+
+def get_embedding(encoder, inputs):
+    outputs = encoder(**inputs)
+    pooled_output = outputs[0][:, 0, :]
+    return pooled_output
+
+tokenizer = BertTokenizer.from_pretrained('xwwwww/bert-chinese-dialogue-retriever-query')
+tokenizer.add_tokens(['<uttsep>'])
+query_encoder = BertModel.from_pretrained('xwwwww/bert-chinese-dialogue-retriever-query')
+context_encoder = BertModel.from_pretrained('xwwwww/bert-chinese-dialogue-retriever-context')
+
+query = '你好<uttsep>好久不见，最近在干嘛'
+context = '正在准备考试<uttsep>是什么考试呀，很辛苦吧'
+
+query_inputs = tokenizer([query], return_tensors='pt')
+context_inputs = tokenizer([context], return_tensors='pt')
+
+query_embedding = get_embedding(query_encoder, query_inputs)
+context_embedding = get_embedding(context_encoder, context_inputs)
+
+score = torch.cosine_similarity(query_embedding, context_embedding, dim=1)
+
+print('similarity score = ', score)
+```