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:
- dc06f0d087b7f8c28b8c3a641dd64f56a789692eb0e90ec3845a93c4f139ff97
- Size of remote file:
- 17.1 MB
- SHA256:
- c9c35b51ba9bc24a2d454d9e418460c8ce47e299a65d8c98d3e65840b2ac0934
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