Instructions to use Qwen/Qwen3-Reranker-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-Reranker-4B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Reranker-4B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-4B") - sentence-transformers
How to use Qwen/Qwen3-Reranker-4B with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Qwen/Qwen3-Reranker-4B") 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
Not able to Reproduce results in MTEB
#12
by AyushM6 - opened
Hello,
I am trying to integrate Qwen3-reranker models(0.6B, 4B, 8B) in MTEB. But we were not able to reproduce the reported results. 1 of the main reasons is that the reported results are doing reranking from Qwen3-embeddings, which we were not able to reproduce. Can any of the co-authors please check the PR in MTEB, mainly if its implementation is correct or not? Its opened from a long time, and we tagged you, but you seem to have missed it.
PR Link: https://github.com/embeddings-benchmark/mteb/pull/3958
Looks like the changes introduced 12-days back were breaking. Our code failed as well.