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)
https://huggingface.co/multi-vector-encoder/merged-GLInt-LateOn/resolve/main/tokenizer.json
hf download hf://multi-vector-encoder/merged-GLInt-LateOn/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/multi-vector-encoder/merged-GLInt-LateOn/resolve/main/tokenizer.json
