Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:286816
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use Jimmy-Ooi/Tyrisonase_test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Jimmy-Ooi/Tyrisonase_test_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jimmy-Ooi/Tyrisonase_test_model") sentences = [ "CC(C)C[C@H](NC(=O)[C@@H](N)Cc1ccccc1)C(=O)NCc1cc(=O)c(O)c[nH]1", "CC(=O)N1CCC(Cc2ccc(F)cc2)CC1", "C=CC(C)(C)c1cc(CCCc2cc(O)c(O)c(CC3OC3(C)C)c2CC=C(C)C)c(O)cc1O", "COc1cc([N+](=O)[O-])ccc1/C=C/C(=N\\O)c1cc2ccccc2cc1O" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle