Text Ranking
sentence-transformers
Safetensors
English
qwen3
finance
legal
code
stem
medical
custom_code
Instructions to use zeroentropy/zerank-1-small-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zeroentropy/zerank-1-small-reranker with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("zeroentropy/zerank-1-small-reranker", trust_remote_code=True) 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
Update model card with paper details and GitHub link
#3
by nielsr HF Staff - opened
This PR significantly enhances the model card for zeroentropy/zerank-1-small by:
- Adding a clear introduction linking the model to its associated paper, zELO: ELO-inspired Training Method for Rerankers and Embedding Models.
- Including the paper's abstract to provide a comprehensive overview of the model's methodology.
- Adding an explicit "Code" section that links to the zbench GitHub repository, improving discoverability of the underlying code and benchmarking framework.
- Removing the internal "File information" section, as it's not relevant for public display.
These updates aim to provide users with accurate and complete information directly on the Hugging Face Hub, making it easier for researchers and developers to understand and utilize the model.