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@@ -8,10 +8,13 @@ This model can be used to generate a SMILES string from an input caption.
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  ## Example Usage
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  ```python
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  from transformers import T5Tokenizer, T5ForConditionalGeneration
 
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  tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512)
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  model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-caption2smiles')
 
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  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.'
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  input_ids = tokenizer(input_text, return_tensors="pt").input_ids
 
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  outputs = model.generate(input_ids, num_beams=5, max_length=512)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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  # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O".
 
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  ## Example Usage
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  ```python
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  from transformers import T5Tokenizer, T5ForConditionalGeneration
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  tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512)
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  model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small-caption2smiles')
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  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.'
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  input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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  outputs = model.generate(input_ids, num_beams=5, max_length=512)
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  print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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  # The model will generate "COC1=C(C=CC(=C1)CCCO)O". The ground-truth is "COC1=C(C=CC(=C1)CO)O".