Instructions to use adriansyahdr/adrBert-base-p1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adriansyahdr/adrBert-base-p1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="adriansyahdr/adrBert-base-p1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("adriansyahdr/adrBert-base-p1") model = AutoModelForMaskedLM.from_pretrained("adriansyahdr/adrBert-base-p1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5edaed027e589f87f5f370d0bb7772309c08cf845dc0b5ec61d10351f7e4b6e
- Size of remote file:
- 504 MB
- SHA256:
- 5b28e33e9e9ea70a8cdb80b52eee52799ce28d599c1f97162de4935e7082d658
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