Text Classification
sentence-transformers
Safetensors
multilingual
cross-encoder
reranker
affiliation-matching
scholarly-metadata
custom_code
Instructions to use cometadata/jina-reranker-v2-multilingual-affiliations-comet-training-only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cometadata/jina-reranker-v2-multilingual-affiliations-comet-training-only with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("cometadata/jina-reranker-v2-multilingual-affiliations-comet-training-only", 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
- Xet hash:
- b8097a87025ddc00a04c7f0766e751b3da50298d9effb2a98cc3a2378901d5b6
- Size of remote file:
- 557 MB
- SHA256:
- 1f5c8245e8ea7211a63b766320b3e0d4c81358ee9838715f8b635a9e9a0861b2
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