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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce0a26e6e14d5cc0cf4a31e0e90d8a6ff8657886afe4f15b11ee10fc9d39d073
- Size of remote file:
- 445 MB
- SHA256:
- 3a32e68c39cd5a0f84a01137fa81ae505ee1b4145fe943d3ee84c052c8248b3d
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