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:
# 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 SentenceTransformer model = SentenceTransformer("webAI-Official/webAI-ColVec1.1-8b", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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