Instructions to use mukund/privbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mukund/privbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mukund/privbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mukund/privbert") model = AutoModelForMaskedLM.from_pretrained("mukund/privbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbaad888a75b194bae05b8326ea446a84f6de51eb45b2f7dcb54de15bc1e61da
- Size of remote file:
- 499 MB
- SHA256:
- 02400b0f6bdb0d9e160ec69028ea4118c59952ff3b0de24dbe7e43790a6f84f0
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