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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 146180803e64de7964f669cd08b166cd391a499bec653265090f711bf9b71758
- Size of remote file:
- 667 MB
- SHA256:
- ce37a3ac65da34f6fd974f99d4e595aaacb9e44a920bc164f55384428e91ddd0
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