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
File size: 657 Bytes
c588694 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"transformer_task": "retrieval",
"modality_config": {
"text": {
"method": "forward",
"method_output_name": null
},
"image": {
"method": "forward",
"method_output_name": null
},
"message": {
"method": "forward",
"method_output_name": null,
"format": "structured"
}
},
"module_output_name": "token_embeddings",
"processing_kwargs": {
"chat_template": {
"chat_template": "sentence_transformers"
},
"text": {
"return_mm_token_type_ids": true
}
}
} |