Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:84996
loss:SoftmaxLoss
text-embeddings-inference
Instructions to use cafierom/smiles_embedding_gemma_FT_full 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_full with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cafierom/smiles_embedding_gemma_FT_full") sentences = [ "O[C@H](C[C@H](O)\\C=C\\c1c2CCCC(Cc3ccc(Cl)cc3)c2nn1-c1ccc(F)cc1)CC([O-])=O", "CC(C)c1sc(c(c1\\C=C\\[C@@H](O)C[C@@H](O)CC([O-])=O)-c1ccccc1)-c1ccccc1", "C[C@H](CC\\C=C(/C)C(O)=O)[C@H]1CC[C@@]2(C)C3=CC[C@H]4C(C)(C)[C@@H](O)CC[C@]4(C)C3=CC[C@]12C", "CCOC(=O)COc1ccc(CC=C)cc1OC" ] 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" | |
| } | |
| ] |