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
Update README.md
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README.md
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doi = {10.1093/nar/gkac1008}
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}
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```
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doi = {10.1093/nar/gkac1008}
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}
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Optimizer:
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@article{wright2021ranger21,
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title={Ranger21: a synergistic deep learning optimizer},
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author={Wright, Less and Demeure, Nestor},
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year={2021},
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journal={arXiv preprint arXiv:2106.13731},
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}
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```
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