Instructions to use kumarme072/med_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumarme072/med_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kumarme072/med_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kumarme072/med_model") model = AutoModelForMaskedLM.from_pretrained("kumarme072/med_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 961850e2ace44dfbd7cab513dc4e20703cf1d95d67467e81f2bf3fe31037de22
- Size of remote file:
- 14.5 MB
- SHA256:
- 6333953c0adb568c3a4d4e8860a9d6feb8f55f22661dcd4160692aac522662be
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