Instructions to use ncbi/MedCPT-Cross-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncbi/MedCPT-Cross-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ncbi/MedCPT-Cross-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Cross-Encoder") model = AutoModelForSequenceClassification.from_pretrained("ncbi/MedCPT-Cross-Encoder") - Inference
- Notebooks
- Google Colab
- Kaggle
Update model metadata to set pipeline tag to the new `text-ranking` and library name to `sentence-transformers`
#4
by tomaarsen HF Staff - opened
README.md
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license: other
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license_name: public-domain
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license_link: LICENSE
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---
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# Usage: Ranking articles for a given query
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license: other
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license_name: public-domain
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license_link: LICENSE
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library_name: sentence-transformers
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pipeline_tag: text-ranking
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---
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# Usage: Ranking articles for a given query
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