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
- Xet hash:
- 7d63357c90ad4f05949db61f673caf7115b6fea18b1e36ac1becc293f812c6eb
- Size of remote file:
- 5.59 kB
- SHA256:
- 99dec7c41dd5b113f9851ebab26ae880e847d9d307814fa0f8fb0754a7db9282
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