Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:67830
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use Jimmy-Ooi/Tyrisonase_test_model_600_6epoch 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_600_6epoch with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jimmy-Ooi/Tyrisonase_test_model_600_6epoch") sentences = [ "C=CC(C)(C)c1cc(C2CC(=O)c3c(O)cc(O)c(CC=C(C)C)c3O2)c(OC)cc1O", "Nc1ccc(C(=O)N2CCN(Cc3ccc(F)cc3)CC2)cc1", "O=C(O)CSc1nnc(NC(=S)Nc2cccc(Cl)c2)s1", "O=C(/C=C/c1ccc(O)c(O)c1)NCCc1c[nH]c2ccc(O)cc12" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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