Instructions to use Su-informatics-lab/bert_base_uncased_rxnorm_babbage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Su-informatics-lab/bert_base_uncased_rxnorm_babbage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Su-informatics-lab/bert_base_uncased_rxnorm_babbage")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Su-informatics-lab/bert_base_uncased_rxnorm_babbage") model = AutoModelForMaskedLM.from_pretrained("Su-informatics-lab/bert_base_uncased_rxnorm_babbage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Su-informatics-lab/bert_base_uncased_rxnorm_babbage: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/Su-informatics-lab/bert_base_uncased_rxnorm_babbage/resolve/main/optimizer.pt
- Command line
-
hf download hf://Su-informatics-lab/bert_base_uncased_rxnorm_babbage/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Su-informatics-lab/bert_base_uncased_rxnorm_babbage/resolve/main/optimizer.pt
876 MB
- Xet hash:
- ba30293f1120974934c206690b4ef4bd4420017fcef44e84e3b964d63ef4b2ad
- Size of remote file:
- 876 MB
- SHA256:
- 85eaef3da135ce7630db7a1de6ac3c9a21908c865a504f7252550f8c85928039
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