Sentence Similarity
sentence-transformers
Safetensors
roberta
chemistry
molecular-similarity
cheminformatics
ssl
smiles
feature-extraction
text-embeddings-inference
Instructions to use gbyuvd/miniChembed-prototype with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use gbyuvd/miniChembed-prototype with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("gbyuvd/miniChembed-prototype") 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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### Testing Similarity Search
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> Tip: For large-scale similarity search, integrate embeddings with Meta's FAISS.
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Cytisine as query, on 24K embedded index:
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### Testing Similarity Search
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> Tip: For large-scale similarity search, integrate embeddings with Meta's FAISS.
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For an example of FAISS indexing pipeline, see `./examples/faiss.ipynb`
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Cytisine as query, on 24K embedded index:
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