readme: ---
  language:
  - ru
  tags:
  - PyTorch
  - Transformers
  ---
  
  # BERT large model (uncased) for Sentence Embeddings in Russian language.
  The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/527576/)  
  For better quality, use mean token embeddings.
  
  ## Usage (HuggingFace Models Repository)
  
  You can use the model directly from the model repository to compute sentence embeddings:
  ```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()
      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
  
  
  
  #Sentences we want sentence embeddings for
  sentences = ['Привет! Как твои дела?',
               'А правда, что 42 твое любимое число?']
  
  #Load AutoModel from huggingface model repository
  tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/sbert_large_nlu_ru")
  model = AutoModel.from_pretrained("sberbank-ai/sbert_large_nlu_ru")
  
  #Tokenize sentences
  encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=24, 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'])
  ```
  