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ibm-research / materials.selfies-ted
Model's Last Updated: October 14 2025
feature-extraction
Introduction of materials.selfies-ted
Model Details of materials.selfies-ted
selfies-ted
selfies-ted is an transformer based encoder decoder model for molecular representations using SELFIES.
Usage
Import
from transformers import AutoTokenizer, AutoModel
import selfies as sf
import torch
Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("ibm/materials.selfies-ted")
model = AutoModel.from_pretrained("ibm/materials.selfies-ted")
Encode SMILES strings to selfies
smiles = "c1ccccc1"
selfies = sf.encoder(smiles)
selfies = selfies.replace("][", "] [")
Get embedding
token = tokenizer(selfies, return_tensors='pt', max_length=128, truncation=True, padding='max_length')
input_ids = token['input_ids']
attention_mask = token['attention_mask']
outputs = model.encoder(input_ids=input_ids, attention_mask=attention_mask)
model_output = outputs.last_hidden_state
input_mask_expanded = attention_mask.unsqueeze(-1).expand(model_output.size()).float()
sum_embeddings = torch.sum(model_output * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
model_output = sum_embeddings / sum_mask
Runs of ibm-research materials.selfies-ted on huggingface.co
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More Information About materials.selfies-ted huggingface.co Model
Url of materials.selfies-ted
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Updated:July 23 2026
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