Feature Extraction
sentence-transformers
Safetensors
qwen3_vl
multi-vector
colbert
late-interaction
Generated from Trainer
dataset_size:10000
loss:CachedMultiVectorMultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use tomaarsen/ColQwen3-VL-Embedding-2B-vdr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/ColQwen3-VL-Embedding-2B-vdr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/ColQwen3-VL-Embedding-2B-vdr") 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
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "text": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| }, | |
| "image": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| }, | |
| "video": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state" | |
| }, | |
| "message": { | |
| "method": "forward", | |
| "method_output_name": "last_hidden_state", | |
| "format": "structured" | |
| } | |
| }, | |
| "module_output_name": "token_embeddings", | |
| "processing_kwargs": { | |
| "chat_template": { | |
| "add_generation_prompt": true | |
| } | |
| }, | |
| "unpad_inputs": false | |
| } |