readme: ---
  pipeline_tag: sentence-similarity
  license: apache-2.0
  tags:
  - text2vec
  - feature-extraction
  - sentence-similarity
  - transformers
  ---
  # shibing624/text2vec-base-chinese
  This is a CoSENT(Cosine Sentence) model: shibing624/text2vec-base-chinese.
  
  It maps sentences to a 768 dimensional dense vector space and can be used for tasks 
  like sentence embeddings, text matching or semantic search.
  
  
  ## Evaluation
  For an automated evaluation of this model, see the *Evaluation Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
  
  - chinese text matching task：
  
  | Model Name | ATEC | BQ | LCQMC | PAWSX | STS-B | Avg | QPS |
  | :---- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
  | w2v-light-tencent-chinese | 20.00 | 31.49 | 59.46 | 2.57 | 55.78 | 33.86 | 10283 |
  | paraphrase-multilingual-MiniLM-L12-v2 | 18.42 | 38.52 | 63.96 | 10.14 | 78.90 | 41.99 | 2371 |
  | text2vec-base-chinese | 31.93 | 42.67 | 70.16 | 17.21 | 79.30 | **48.25** | 2572 |
  
  
  ## Usage (text2vec)
  Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
  
  ```
  pip install -U text2vec
  ```
  
  Then you can use the model like this:
  
  ```python
  from text2vec import SentenceModel
  sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
  
  model = SentenceModel('shibing624/text2vec-base-chinese')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  ## Usage (HuggingFace Transformers)
  Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: 
  
  First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
  
  Install transformers:
  ```
  pip install transformers
  ```
  
  Then load model and predict:
  ```python
  from transformers import BertTokenizer, BertModel
  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)
  
  # Load model from HuggingFace Hub
  tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese')
  model = BertModel.from_pretrained('shibing624/text2vec-base-chinese')
  sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
  # 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, max pooling.
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  ## Usage (sentence-transformers)
  [sentence-transformers](https://github.com/UKPLab/sentence-transformers) is a popular library to compute dense vector representations for sentences.
  
  Install sentence-transformers:
  ```
  pip install -U sentence-transformers
  ```
  
  Then load model and predict:
  
  ```python
  from sentence_transformers import SentenceTransformer
  
  m = SentenceTransformer("shibing624/text2vec-base-chinese")
  sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
  
  sentence_embeddings = m.encode(sentences)
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  
  ## Full Model Architecture
  ```
  CoSENT(
    (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
  )
  ```
  ## Citing & Authors
  This model was trained by [text2vec](https://github.com/shibing624/text2vec). 
          
  If you find this model helpful, feel free to cite:
  ```bibtex 
  @software{text2vec,
    author = {Xu Ming},
    title = {text2vec: A Tool for Text to Vector},
    year = {2022},
    url = {https://github.com/shibing624/text2vec},
  }
  ```
  