Feature Extraction
sentence-transformers
Safetensors
English
modernbert
multi-vector
colbert
late-interaction
text-embeddings-inference
Instructions to use multi-vector-encoder/merged-GLInt-LateOn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use multi-vector-encoder/merged-GLInt-LateOn with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("multi-vector-encoder/merged-GLInt-LateOn") 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
Commit History
Yesterday
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Rename model card to merged-GLInt-LateOn
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4 days ago
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Update README.md
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Lower ST version from dev version
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Add new MultiVectorEncoder model
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initial commit
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