readme: ---
  language: en
  thumbnail:
  tags:
  - bert
  - embeddings
  license: apache-2.0
  ---
  
  # LABSE BERT
  
  ## Model description
  
  Model for "Language-agnostic BERT Sentence Embedding" paper from Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang. Model available in [TensorFlow Hub](https://tfhub.dev/google/LaBSE/1).
  
  ## Intended uses & limitations
  
  #### How to use
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  import torch
  
  # from sentence-transformers
  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()
      sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
      sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
      return sum_embeddings / sum_mask
  
  tokenizer = AutoTokenizer.from_pretrained("pvl/labse_bert", do_lower_case=False)
  model = AutoModel.from_pretrained("pvl/labse_bert")
  
  sentences = ['This framework generates embeddings for each input sentence',
               'Sentences are passed as a list of string.',
               'The quick brown fox jumps over the lazy dog.']
  
  encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')
  
  with torch.no_grad():
      model_output = model(**encoded_input)
  
  sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
  
  
  ```
  