Instructions to use Kaspar/bert_pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaspar/bert_pretrained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Kaspar/bert_pretrained")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Kaspar/bert_pretrained") model = AutoModelForMaskedLM.from_pretrained("Kaspar/bert_pretrained") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6414f1a61f9c618da71d94a3a7a0c9d6276404dec755132ab679a6ef6337b553
- Size of remote file:
- 5.84 kB
- SHA256:
- 61e15c02e7389936343fe838b7e47f9cd3d1f92c2914322484d33dcf1dcd0867
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.