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license: apache-2.0 |
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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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``` |
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## Paper |
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For more information, please take a look at our paper. |
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Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817) |
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Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji* |
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