Sentence Similarity
sentence-transformers
Safetensors
English
esm
protein
esm2
contrastive-learning
protein-embeddings
biology
Instructions to use GrimSqueaker/ProtSent-V2-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GrimSqueaker/ProtSent-V2-150M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GrimSqueaker/ProtSent-V2-150M") 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
Commit History
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Correct the alignment comparison: filters-off phmmer leads at top-1
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Model card: normal card, no DMS claim, training config and results
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ProtSent-V2-150M: retrained on the 40%/80% decontaminated corpus
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initial commit
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