Instructions to use RUI525/SapBERT-finetune-MedMCQA-w-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RUI525/SapBERT-finetune-MedMCQA-w-context with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("RUI525/SapBERT-finetune-MedMCQA-w-context") model = AutoModelForMultipleChoice.from_pretrained("RUI525/SapBERT-finetune-MedMCQA-w-context", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RUI525/SapBERT-finetune-MedMCQA-w-context: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/RUI525/SapBERT-finetune-MedMCQA-w-context/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RUI525/SapBERT-finetune-MedMCQA-w-context/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RUI525/SapBERT-finetune-MedMCQA-w-context/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- eff5fa6e6a2e34a34580adf6eba1c84ccc7161e6a3d2d4469410d7a84154822c
- Size of remote file:
- 438 MB
- SHA256:
- 00773c98874fbb10073bbcbbc1cebf0e9f3fcd397ad3144d542c5c5036244ef3
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