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:
- 98f618ceb3fc165cc9151346e01f5cb959c6917c853c46f929617dacbd8a338a
- Size of remote file:
- 9.37 kB
- SHA256:
- bb9f2d612fb7091cd31cc474c765c7411a0aadda8549a8f227b2b35faf35c1fa
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