Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:116941
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use cafierom/905_Statin_Contrastive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cafierom/905_Statin_Contrastive with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cafierom/905_Statin_Contrastive") sentences = [ "O[C@@H]1CC(CCc2c(O)cc(Cl)cc2Cl)OC(=O)C1", "O[C@@H]1C[C@@H](CC[C@@H]2CCC[C@@H]3CCCC[C@H]23)OC(=O)C1", "O[C@@H]1CC(CCc2cccc3ccccc23)OC(=O)C1", "CC(C)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1ccccc1)-c1ccccn1)-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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| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
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| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
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