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:
- ee95420d8a7fc00eadccc785ff77e5937a9ce23fd65593255d67e00462e0604e
- Size of remote file:
- 13 MB
- SHA256:
- 5b75a6866258402b83047e5573602859c17e4ea3945b899dba747de7e096b55d
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