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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from wietsedv/bert-base-dutch-cased: direct link, hf CLI and curl.
- Browser
- Download file 437 MB
-
https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://wietsedv/bert-base-dutch-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/flax_model.msgpack
437 MB
- Xet hash:
- b706c44dc5bd1aa669c805883083d498fecf574b48775d884a127b4710f211d5
- Size of remote file:
- 437 MB
- SHA256:
- 7b6b447d93ce795afffe7ab6c0dc856d5ae4fdd6fc80916b19a19297331df1d1
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