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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff3b78ee8233ca353867259c1bd8e2b2a0638f90ae98283a1cce932a43bec321
- Size of remote file:
- 373 MB
- SHA256:
- 3214694a69f9591b8084e243ee48198884c2a0d103a7550f3360021a743c1694
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