Instructions to use naver/trecdl22-crossencoder-debertav3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/trecdl22-crossencoder-debertav3 with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("naver/trecdl22-crossencoder-debertav3") 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
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Download README.md from naver/trecdl22-crossencoder-debertav3: direct link, hf CLI and curl.
- Browser
- Download file 96 Bytes
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https://huggingface.co/naver/trecdl22-crossencoder-debertav3/resolve/main/README.md
- Command line
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hf download hf://naver/trecdl22-crossencoder-debertav3/README.md
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curl -L -o README.md https://huggingface.co/naver/trecdl22-crossencoder-debertav3/resolve/main/README.md
96 Bytes
metadata
license: cc-by-nc-sa-4.0
library_name: sentence-transformers
pipeline_tag: text-ranking