Sentence Similarity
sentence-transformers
Safetensors
roberta
smiles-similarity
feature-extraction
molecular-similarity
text-embeddings-inference
Instructions to use Derify/ChemMRL-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Derify/ChemMRL-alpha with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Derify/ChemMRL-alpha") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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# SentenceTransformer
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This is a trained [Chem-MRL](https://github.com/emapco/chem-mrl) [sentence-transformers](https://www.SBERT.net) model. It maps SMILES to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, database indexing, molecular classification, clustering, and more.
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# Chem-MRL (SentenceTransformer)
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This is a trained [Chem-MRL](https://github.com/emapco/chem-mrl) [sentence-transformers](https://www.SBERT.net) model. It maps SMILES to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, database indexing, molecular classification, clustering, and more.
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