Instructions to use sm6342/Health101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sm6342/Health101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sm6342/Health101")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sm6342/Health101") model = AutoModelForMaskedLM.from_pretrained("sm6342/Health101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2814e56d0af1aedb31e4846ec304b36663f55a6ba024308394cfd4d554abb83
- Size of remote file:
- 389 MB
- SHA256:
- 9275fe9895e7a1461ea4948eb7fce87753f6fb408d3269b519abf77ff59d7694
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.