Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:2184
loss:BatchAllTripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use kiarashmo/chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kiarashmo/chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kiarashmo/chembberta-77m-sBERT-finetuned-on-clintox-with-sBERTBatchAllTripletLoss") sentences = [ "CC(C)CNCc1ccc(-c2ccccc2S(=O)(=O)N2CCCC2)cc1", "C=C1CC2CCC34CC5OC6C(OC7CCC(CC(=O)CC8C(CC9OC(CCC1O2)CC(C)C9=C)OC(CC(O)CN)C8OC)OC7C6O3)C5O4", "C[NH+](C)CCC=C1c2ccccc2CCc2ccccc21", "CC(C)Cn1cnc2c(N)nc3ccccc3c21" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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