Feature Extraction
sentence-transformers
Safetensors
sentence-similarity
biomedical
embeddings
life-sciences
scientific-text
SODA-VEC
EMBO
Instructions to use EMBO/vicreg_our with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use EMBO/vicreg_our with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("EMBO/vicreg_our") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc2d14d8501f89dda085b32469dff111896a72f28e3fc94cd7cf6abaa665195b
- Size of remote file:
- 6.07 kB
- SHA256:
- 31b7791c285ea5a9e6b6cbb04aabbef87ebbc77ffb50809c3cce61b0c4f06eaf
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