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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from ncbi/MedCPT-Cross-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ncbi/MedCPT-Cross-Encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ncbi/MedCPT-Cross-Encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ncbi/MedCPT-Cross-Encoder/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 52dc7135fdc8071a75e91115a7e75a20821a13395107fdc9c95b0c96b119207f
- Size of remote file:
- 438 MB
- SHA256:
- 61d5ccd48869e03500544525fc231641d7daa9ba267b202c82724750038dc1e0
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