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da4mt-mtr-60
UdS-LSV/da4mt-mtr-60
da4mt-mtr-60 is a machine learning model from UdS-LSV. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This repository contains the model weights of the BERT model trained by predicting RDKit physio-chemical properties on 30% of the GuacaMol dataset. Further information can be found in our publication.
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At a glance
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- May 12, 2025
- Updated
- May 19, 2025
- SHA
- 9dbdb85b
Model card
From the Hugging Face model README
This repository contains the model weights of the BERT model trained by predicting RDKit physio-chemical properties on 30% of the GuacaMol dataset. Further information can be found in our publication.
from transformers import AutoModel, AutoTokenizer
mols = [
"CCOc1cc2nn(CCC(C)(C)O)cc2cc1NC(=O)c1cccc(C(F)F)n1",
"CN(c1ncc(F)cn1)[C@H]1CCCNC1",
"CC(C)(Oc1ccc(-c2cnc(N)c(-c3ccc(Cl)cc3)c2)cc1)C(=O)O",
"CC(C)(O)CCn1cc2cc(NC(=O)c3cccc(C(F)(F)F)n3)c(C(C)(C)O)cc2n1",
# ...
]
tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/da4mt-mtr-60")
model = AutoModel.from_pretrained("UdS-LSV/da4mt-mtr-60")
inputs = tokenizer(mols, add_special_tokens=True, truncation=True, max_length=128, padding="max_length", return_tensors="pt")
embeddings = model(**inputs).last_hidden_state[:, 0, :]
See also
- Type
- bert
- License
- mit
- Created
- May 12, 2025
- Updated
- May 19, 2025