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