Sentence Similarity
sentence-transformers
Safetensors
roberta
molecular-similarity
feature-extraction
dense
Generated from Trainer
loss:Matryoshka2dLoss
loss:MatryoshkaLoss
loss:TanimotoSentLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Derify/ChemMRL-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Derify/ChemMRL-beta with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Derify/ChemMRL-beta") sentences = [ "CC1CCc2c(N)nc(C3CCCC3)n2C1", "CC1CCc2c(N)nc(OC3CC3)n2C1", "CN1CC[NH+](C[C@H](O)C2CC2)C2(CCCCC2)C1", "Cc1c(F)cc(CNCC2CCC(C3CCC(C)CO3)CO2)cc1F" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |