Instructions to use ncbi/MedCPT-Query-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncbi/MedCPT-Query-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ncbi/MedCPT-Query-Encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ncbi/MedCPT-Query-Encoder") model = AutoModel.from_pretrained("ncbi/MedCPT-Query-Encoder", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ncbi/MedCPT-Query-Encoder: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ncbi/MedCPT-Query-Encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ncbi/MedCPT-Query-Encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ncbi/MedCPT-Query-Encoder/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 14021ccdc6cc0d70bad3400e4b22a5d7611419990938fb75bcfd9c1c257e1953
- Size of remote file:
- 438 MB
- SHA256:
- debe354238bfd65f4f059d37e9c49a41c0908a2584d7a9328fc3155865a0fe9b
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