Instructions to use dbmdz/bert-base-historic-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-base-historic-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-base-historic-multilingual-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-historic-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-base-historic-multilingual-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c55d8ca5d9649898f701cb5aa810be98a80d0c38d81d4c450a262831d181f04
- Size of remote file:
- 4.63 MB
- SHA256:
- 500990f59abdeebc8c4ea367a026d43a44d7a702e39542c4c0f2bb050f2e4dc8
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