Sentence Similarity
sentence-transformers
Safetensors
English
esm
protein
esm2
contrastive-learning
protein-embeddings
biology
Instructions to use GrimSqueaker/ProtSent-V2-35M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GrimSqueaker/ProtSent-V2-35M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GrimSqueaker/ProtSent-V2-35M") 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:
- c8507c6a4cdbf93fa53cc5e552a7fa44e08274d22c828ae6f2740acdf91659ae
- Size of remote file:
- 5.59 kB
- SHA256:
- 9e8109e5ee1dd0316deb6d3d02c7a5748a109611e8d6a6930f8b96811b858fe2
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