Instructions to use dbmdz/bert-base-historic-dutch-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-base-historic-dutch-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-base-historic-dutch-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-historic-dutch-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-base-historic-dutch-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 28ca9f97feb546779a2fe00840d0834e80b14acaabfe51a6ad0ae3489f9ed5a0
- Size of remote file:
- 1.33 GB
- SHA256:
- a5b39f6b6a060f0e48d2b8d75fdf429a9695b1e887d6394a028d445125db1df3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.