readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  
  widget:
  - source_sentence: "[*]CC[*]"
    sentences:
      - "[*]COC[*]"
      - "[*]CC(C)C[*]"
  ---
  
  # kuelumbus/polyBERT
  
  This is polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics. polyBERT maps PSMILES strings to 600 dimensional dense fingerprints. The fingerprints numerically represent polymer chemical structures. Please see the license agreement in the LICENSE file.
  
  <!--- Describe your model here -->
  
  ## Usage (Sentence-Transformers)
  
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
  
  ```
  pip install sentence-transformers
  ```
  
  Then you can use the model like this:
  
  ```python
  from sentence_transformers import SentenceTransformer
  psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
  
  polyBERT = SentenceTransformer('kuelumbus/polyBERT')
  embeddings = polyBERT.encode(psmiles_strings)
  print(embeddings)
  ```
  
  
  
  ## Usage (HuggingFace Transformers)
  Without [sentence-transformers](https://www.SBERT.net), 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.
  
  ```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()
      return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
  
  
  # Sentences we want sentence embeddings for
  psmiles_strings = ["[*]CC[*]", "[*]COC[*]"]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('kuelumbus/polyBERT')
  polyBERT = AutoModel.from_pretrained('kuelumbus/polyBERT')
  
  # Tokenize sentences
  encoded_input = tokenizer(psmiles_strings, padding=True, truncation=True, return_tensors='pt')
  
  # Compute token embeddings
  with torch.no_grad():
      model_output = polyBERT(**encoded_input)
  
  # Perform pooling. In this case, mean pooling.
  fingerprints = mean_pooling(model_output, encoded_input['attention_mask'])
  
  print("Fingerprints:")
  print(fingerprints)
  ```
  
  
  
  ## Evaluation Results
  
  See https://github.com/Ramprasad-Group/polyBERT and paper on arXiv.
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
    (1): Pooling({'word_embedding_dimension': 600, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  )
  ```
  
  ## Citing & Authors
  
  t.b.d.