Instructions to use Riiid/kda-biobert-race with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Riiid/kda-biobert-race with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Riiid/kda-biobert-race") model = AutoModelForMultipleChoice.from_pretrained("Riiid/kda-biobert-race", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Riiid/kda-biobert-race: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/Riiid/kda-biobert-race/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Riiid/kda-biobert-race/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Riiid/kda-biobert-race/resolve/main/pytorch_model.bin
433 MB
- Xet hash:
- e6356d0d4d7ea461ae8dcd6582f45ca97fb7f8d69e73103697b1cadb63885d69
- Size of remote file:
- 433 MB
- SHA256:
- f8bd4848700d6fdaa4de51b26b65e5f4142f09421d983096c0e6feda38f6fb95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.