readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - semantic-search
  - chinese
  ---
  
  # DMetaSoul/sbert-chinese-qmc-domain-v1
  
  此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百度知道问题匹配数据集（[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)）上进行训练调优，适用于**开放领域的问题匹配**场景，比如：
  
  - 洗澡用什么香皂好？vs. 洗澡用什么香皂好
  - 大连哪里拍婚纱照好点？ vs. 大连哪里拍婚纱照比较好
  - 银行卡怎样挂失？vs. 银行卡丢了怎么挂失啊？
  
  注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-qmc-domain-v1-distill)，也已经开源啦！
  
  # Usage
  
  ## 1. Sentence-Transformers
  
  通过  [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
  
  ```
  pip install -U sentence-transformers
  ```
  
  然后使用下面的代码来载入该模型并进行文本表征向量的提取：
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
  
  model = SentenceTransformer('DMetaSoul/sbert-chinese-qmc-domain-v1')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  ## 2. HuggingFace Transformers
  
  如果不想使用   [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  import torch
  
  
  #Mean Pooling - Take attention mask into account for correct averaging
  def mean_pooling(model_output, attention_mask):
      token_embeddings = model_output[0] #First element of model_output contains all token embeddings
      input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
      return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
  
  
  # Sentences we want sentence embeddings for
  sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
  model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-qmc-domain-v1')
  
  # Tokenize sentences
  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
  
  # Compute token embeddings
  with torch.no_grad():
      model_output = model(**encoded_input)
  
  # Perform pooling. In this case, mean pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  ## Evaluation
  
  该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数：
  
  |                                 | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** |
  | ------------------------------- | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- |
  | **sbert-chinese-qmc-domain-v1** | 80.90%       | 76.63%        | 34.51%    | 77.06%    | 52.96%       | 12.98%    | 59.48%     |
  
  ## Citing & Authors
  
  E-mail: xiaowenbin@dmetasoul.com