readme: ---
  license: apache-2.0
  ---
  
  
  This model can be used to generate a SMILES string from an input caption.
  
  ## Example Usage
  ```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  
  tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512)
  model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-caption2smiles')
  
  input_text = 'The molecule is a monomethoxybenzene that is 2-methoxyphenol substituted by a hydroxymethyl group at position 4. It has a role as a plant metabolite. It is a member of guaiacols and a member of benzyl alcohols.'
  input_ids = tokenizer(input_text, return_tensors="pt").input_ids
  
  outputs = model.generate(input_ids, num_beams=5, max_length=512)
  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
  # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O".
  ```
  
  ## Paper
  
  For more information, please take a look at our paper.
  
  Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
  
  Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji* 
  