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
File size: 677 Bytes
2418759 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | [
{
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"name": "0",
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"type": "sentence_transformers.base.modules.transformer.Transformer"
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{
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"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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{
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{
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"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
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