Visual Document Retrieval
Transformers
Safetensors
sentence-transformers
multilingual
qwen3_5
feature-extraction
text
image
multimodal-embedding
vidore
colbert
colqwen3_5
multilingual-embedding
multi-vector
custom_code
Instructions to use webAI-Official/webAI-ColVec1.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webAI-Official/webAI-ColVec1.1-8b with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("webAI-Official/webAI-ColVec1.1-8b", trust_remote_code=True) model = AutoModel.from_pretrained("webAI-Official/webAI-ColVec1.1-8b", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use webAI-Official/webAI-ColVec1.1-8b with sentence-transformers:
from sentence_transformers import MultiVectorEncoder model = MultiVectorEncoder("webAI-Official/webAI-ColVec1.1-8b", 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) - Notebooks
- Google Colab
- Kaggle
File size: 133 Bytes
75a05e8 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
size 19989325
|