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-ColVec1.1-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webAI-Official/webAI-ColVec1.1-4b with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("webAI-Official/webAI-ColVec1.1-4b", trust_remote_code=True) model = AutoModel.from_pretrained("webAI-Official/webAI-ColVec1.1-4b", trust_remote_code=True, device_map="auto") - sentence-transformers
How to use webAI-Official/webAI-ColVec1.1-4b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("webAI-Official/webAI-ColVec1.1-4b", 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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_MultiVectorMask", | |
| "type": "sentence_transformers.multi_vector_encoder.modules.multi_vector_mask.MultiVectorMask" | |
| } | |
| ] |