Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
Generated from Trainer
dataset_size:3396
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use cafierom/smiles_embedding_gemma_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cafierom/smiles_embedding_gemma_FT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cafierom/smiles_embedding_gemma_FT") 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)n1c(CC[C@@H](O)C[C@@H](O)CC([O-])=O)c(c(c1C(=O)NCc1ccccn1)-c1ccccc1)-c1ccc(F)cc1", "Cc1cc(OCc2ccccc2)cc(C)c1\\C=C\\[C@@H]1C[C@@H](O)CC(=O)O1", "Cc1ccc(C2CC3CCC2C=C3)n1CC[C@@H]1C[C@@H](O)CC(=O)O1" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.base.modules.dense.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Dense", | |
| "type": "sentence_transformers.base.modules.dense.Dense" | |
| }, | |
| { | |
| "idx": 4, | |
| "name": "4", | |
| "path": "4_Normalize", | |
| "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize" | |
| } | |
| ] |