Instructions to use dbmdz/bert-small-historic-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbmdz/bert-small-historic-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dbmdz/bert-small-historic-multilingual-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-small-historic-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("dbmdz/bert-small-historic-multilingual-cased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 157b9a6f5bf3a176059390385365009a3d2b86e065bf93b76b05c5b13a5df2f8
- Size of remote file:
- 358 MB
- SHA256:
- 12e485c379e5de442fa090aeb4f62355c8cde6e904d14a8b74335a1c1c7567a1
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