Feature Extraction
sentence-transformers
Safetensors
embedding_gemma2
multi-vector
late-interaction
colbert
meanmaxsim
multimodal
text
image
audio
embeddinggemma-2
matryoshka
Instructions to use JacobLinCool/embeddinggemma-2-multimodal-late-interaction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use JacobLinCool/embeddinggemma-2-multimodal-late-interaction with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("JacobLinCool/embeddinggemma-2-multimodal-late-interaction") queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
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