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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83868b588c762dc6c81da285ef5201efc52932a522fc25a5742bc2ab427e1a2a
- Size of remote file:
- 667 MB
- SHA256:
- f1ea5a87ed72ca1ea14d7e9d6742130064151ec48b3bc2ccc492141df509db3e
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