Instructions to use wietsedv/bert-base-dutch-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wietsedv/bert-base-dutch-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="wietsedv/bert-base-dutch-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("wietsedv/bert-base-dutch-cased") model = AutoModelForMaskedLM.from_pretrained("wietsedv/bert-base-dutch-cased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15607bdbecc5c8cd572841acf4fdbb5702f1b4fd68a3db1fc05aedbab32066d3
- Size of remote file:
- 439 MB
- SHA256:
- 0da32020b1799e175f53caddbc6bf250af61d67d5a8c77f4708af6ebe1d03600
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