How to use vidore/colpali-v1.3-hf with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("vidore/colpali-v1.3-hf") model = AutoModelForPreTraining.from_pretrained("vidore/colpali-v1.3-hf", device_map="auto")
How to use vidore/colpali-v1.3-hf 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
How to use vidore/colpali-v1.3-hf with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("vidore/colpali-v1.3-hf") 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)
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