Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:148695
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use cafierom/5Epoch_905_Statin_Contrastive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cafierom/5Epoch_905_Statin_Contrastive with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cafierom/5Epoch_905_Statin_Contrastive") sentences = [ "C[C@H](CCCC(C)(C)O)[C@H]1CC[C@H]2[C@@H]3CC=C4C[C@@H](O)CC[C@]4(C)[C@H]3CC[C@]12C", "CC[C@H](C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CCC(O)C[C@@H](O)CC([O-])=O)[C@@H]12", "C[C@@H]1C[C@H](OC(C)=O)C2[C@@H](CC[C@@H]3C[C@@H](O)CC(=O)O3)[C@@H](C)C=CC2=C1", "CC(C)c1nc(c(-c2ccc(F)cc2)n1CC[C@@H](O)C[C@@H](O)CC([O-])=O)-c1ccc(F)cc1" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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