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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
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# Citation
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If you find this repo helpful, please cite MedCPT by:
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@article{jin2023medcpt,
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title={MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval},
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author={Jin, Qiao and Kim, Won and Chen, Qingyu and Comeau, Donald C and Yeganova, Lana and Wilbur, W John and Lu, Zhiyong},
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pages={btad651},
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year={2023},
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publisher={Oxford University Press}
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}
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# Citation
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If you find this repo helpful, please cite MedCPT by:
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```bibtext
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@article{jin2023medcpt,
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title={MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval},
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author={Jin, Qiao and Kim, Won and Chen, Qingyu and Comeau, Donald C and Yeganova, Lana and Wilbur, W John and Lu, Zhiyong},
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pages={btad651},
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year={2023},
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publisher={Oxford University Press}
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}
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```
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