Text Ranking
Transformers
Safetensors
sentence-transformers
qwen3_vl
image-text-to-text
multimodal rerank
text rerank
Instructions to use Qwen/Qwen3-VL-Reranker-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-VL-Reranker-2B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-Reranker-2B") model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3-VL-Reranker-2B") - sentence-transformers
How to use Qwen/Qwen3-VL-Reranker-2B with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Qwen/Qwen3-VL-Reranker-2B") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Improve model card: add pipeline tag, library name, and paper link
#8
by nielsr HF Staff - opened
Hi! I'm Niels from the Hugging Face community science team.
This PR improves the model card for this reranker model by:
- Adding the
text-rankingpipeline tag to help users discover the model. - Adding
library_name: transformersas the model is compatible with the library according to the requirements and usage scripts. - Adding a link to the original research paper at the top of the README.
- Adding the
base_modelinformation in the metadata.
Please review and merge if this looks good!