Instructions to use Menghuan1918/slide-bge-reranker-v2-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Menghuan1918/slide-bge-reranker-v2-m3 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Menghuan1918/slide-bge-reranker-v2-m3") 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
Model Description
slide-bge-reranker-v2-m3 is a domain-optimized reranking model specifically enhanced for educational scenarios involving slide-based content. Built upon the foundation of bge-reranker-v2-m3, this model has been fine-tuned using a custom dataset to better understand and process academic presentation materials like lecture slides, courseware, and educational resources.
Intended Use
This model is designed for educational applications including but not limited to:
- Lecture slide retrieval systems
- Courseware recommendation engines
- Academic content search augmentation
- Educational Q&A systems
- Curriculum-aligned resource discovery
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