Instructions to use GroNLP/bert-base-dutch-cased-gronings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/bert-base-dutch-cased-gronings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="GroNLP/bert-base-dutch-cased-gronings")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("GroNLP/bert-base-dutch-cased-gronings") model = AutoModelForMaskedLM.from_pretrained("GroNLP/bert-base-dutch-cased-gronings") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd79447201896905fc75eb06d20f13f50e9254a55466a98810cdf7037012684e
- Size of remote file:
- 373 MB
- SHA256:
- a8ed2d1923bad8431f950d7572f1ec4db1c03ef47b65e755111ba0bcb9d57fb3
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