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--- |
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license: apache-2.0 |
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library_name: transformers |
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pipeline_tag: feature-extraction |
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tags: |
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- chemistry |
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- transformers |
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--- |
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# selfies-ted |
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selfies-ted is an transformer based encoder decoder model for molecular representations using SELFIES. |
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## Usage |
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### Import |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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import selfies as sf |
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import torch |
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``` |
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### Load the model and tokenizer |
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``` |
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tokenizer = AutoTokenizer.from_pretrained("ibm/materials.selfies-ted") |
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model = AutoModel.from_pretrained("ibm/materials.selfies-ted") |
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``` |
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### Encode SMILES strings to selfies |
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``` |
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smiles = "c1ccccc1" |
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selfies = sf.encoder(smiles) |
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selfies = selfies.replace("][", "] [") |
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``` |
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### Get embedding |
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``` |
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token = tokenizer(selfies, return_tensors='pt', max_length=128, truncation=True, padding='max_length') |
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input_ids = token['input_ids'] |
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attention_mask = token['attention_mask'] |
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outputs = model.encoder(input_ids=input_ids, attention_mask=attention_mask) |
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model_output = outputs.last_hidden_state |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(model_output.size()).float() |
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sum_embeddings = torch.sum(model_output * input_mask_expanded, 1) |
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sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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model_output = sum_embeddings / sum_mask |
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``` |
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