Instructions to use yosshstd/ProTrek_650M_UniRef50_structure_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yosshstd/ProTrek_650M_UniRef50_structure_encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yosshstd/ProTrek_650M_UniRef50_structure_encoder") 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:
- 5ddc0029b5162e8513ab7b113736bf5f8226f729615a3f5857b001e2281f83a1
- Size of remote file:
- 2.63 MB
- SHA256:
- 70ddbf059e4761af278d0059a1aada96d6341f6009c11e1a9f91b3ceb4b45a5b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.