readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - causal-lm
  license:
  - cc-by-sa-4.0
  ---
  
  # TODO: Name of Model
  
  TODO: Description
  
  ## Model Description
  TODO: Add relevant content
  
  (0) Base Transformer Type: RobertaModel
  
  (1) Pooling mean
  
  
  ## 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(TODO)
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  ## Usage (HuggingFace Transformers)
  
  ```python
  from transformers import AutoTokenizer, AutoModel
  import torch
  
  # The next step is optional if you want your own pooling function.
  # Max Pooling - Take the max value over time for every dimension. 
  def max_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()
      token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
      max_over_time = torch.max(token_embeddings, 1)[0]
      return max_over_time
  
  # Sentences we want sentence embeddings for
  sentences = ['This is an example sentence']
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained(TODO)
  model = AutoModel.from_pretrained(TODO)
  
  # 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 = max_pooling(model_output, encoded_input['attention_mask'])
  
  print("Sentence embeddings:")
  print(sentence_embeddings)
  ```
  
  
  
  ## TODO: Training Procedure
  
  ## TODO: Evaluation Results
  
  ## TODO: Citing & Authors
  