Duplicated from athrael-soju/ColUltraVBERT
How to use vllm-project/ColUltraVBERT with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("vllm-project/ColUltraVBERT", trust_remote_code=True) 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)
How to use vllm-project/ColUltraVBERT with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
https://huggingface.co/vllm-project/ColUltraVBERT/resolve/main/3_Normalize/config.json
hf download hf://vllm-project/ColUltraVBERT/3_Normalize/config.json
curl -L -o config.json https://huggingface.co/vllm-project/ColUltraVBERT/resolve/main/3_Normalize/config.json
