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
- Xet hash:
- c30834c2a182d3bf7cae09fa76068bb4e5a4c74f2b8e9a792c3fc90119d5caff
- Size of remote file:
- 31.7 MB
- SHA256:
- b0a46750ae81fd05a9c2cf7305fd931ea29801d48fc563d96692a89aa2758436
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