Instructions to use kumarme072/med_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumarme072/med_model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kumarme072/med_model_1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kumarme072/med_model_1") model = AutoModelForMaskedLM.from_pretrained("kumarme072/med_model_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ab89ddef4dc06285513bb0cd545eab7a841e8ec99c547e2727e433b47d7d22d
- Size of remote file:
- 13.6 MB
- SHA256:
- 817610fb241564c94178950fb2dc71e7314464a6680ad66ea7c89dff0ded0f5e
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