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
Download model.safetensors from multi-vector-encoder/merged-GLInt-LateOn: direct link, hf CLI and curl.
- Browser
- Download file 596 MB
-
https://huggingface.co/multi-vector-encoder/merged-GLInt-LateOn/resolve/main/model.safetensors
- Command line
-
hf download hf://multi-vector-encoder/merged-GLInt-LateOn/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/multi-vector-encoder/merged-GLInt-LateOn/resolve/main/model.safetensors
596 MB
- Xet hash:
- 7d27f8b50db79f28e271e9cd43e1217ab04c926e7e57de97a080661a2221b708
- Size of remote file:
- 596 MB
- SHA256:
- 6f0ac605ba5687101f96281169c8b4f963a73d4f7aac2b8225de2040c3fb17d0
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