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)
```