Instructions to use kumarme072/model_med_195_E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kumarme072/model_med_195_E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kumarme072/model_med_195_E")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kumarme072/model_med_195_E") model = AutoModelForMaskedLM.from_pretrained("kumarme072/model_med_195_E") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 525332bb8b0581d973ea13396fded14c7de1af36dd0bcac60d852fd7d2f84c6a
- Size of remote file:
- 4.86 kB
- SHA256:
- fc3292f391bf6891f622f576a13489828b1611f798b52fe5e3cae10f8cdcff98
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