Instructions to use intfloat/simlm-msmarco-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/simlm-msmarco-reranker with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("intfloat/simlm-msmarco-reranker") 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 metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers` (#3)
Browse files- Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers` (3d8c5fd8682d497bb74a2f8aadf85c5180d739c3)
Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>
README.md
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license: mit
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language:
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---
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# SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
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license: mit
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language:
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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---
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# SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval
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