readme: ---
  tags:
  - sentence-transformers
  - feature-extraction
  ---
  
  # Name of Model
  
  <!--- Describe your model here -->
  
  ## Model Description
  The model consists of the following layers:
  
  (0) Base Transformer Type: RobertaModel
  
  (1) mean Pooling
  
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes more convenient when you have [sentence-transformers](https://github.com/UKPLab/sentence-transformers) installed:
  
  ```
  pip install -U sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["This is an example sentence"]
  
  model = SentenceTransformer('model_name')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  ## Usage (HuggingFace Transformers)
  
  ```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 = ['This is an example sentence']
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('model_name')
  model = AutoModel.from_pretrained('model_name')
  
  # Tokenize sentences
  encoded_input = tokenizer(sentences, padding=True, truncation=True, max_length=128, 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)
  ```
  
  
  
  ## Training Procedure
  
  <!--- Describe how your model was trained -->
  
  ## Evaluation Results
  
  <!--- Describe how your model was evaluated -->
  
  ## Citing & Authors
  
  <!--- Describe where people can find more information -->
  